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-Powered Insights from #damus Conversations on Nostr Using n8n Automation
   
   Meta Description:  
   Discover how an n8n automation workflow leverages AI-powered tools like Google Gemini and integrates with Gmail, Telegram, and Nostr to analyze and report themes from #damus-tagged threads, offering deep community insights and actionable recommendations.
   
   Keywords:  
   n8n, #damus, Nostr, automated reporting, Google Gemini, AI workflow, Damus insights, LangChain, Gmail integration, Telegram bot, social media analysis, theme extraction, community engagement
   
   Third-Party APIs/Services Used:
   
   1. Nostr (via n8n community node nostrobots)
   2. Google Gemini (PalM API via LangChain)
   3. Gmail (OAuth2 API by Google)
   4. Telegram Bot API
   
   —
   
   Article:
   
   Unlocking Community Intelligence on Nostr with AI: An Automated #damus Reporting Workflow in n8n
   
   In today's decentralized digital landscape, staying ahead of community insights requires more than just monitoring hashtags — it demands smart automation. Meet the #️⃣Nostr #damus AI Powered Reporting workflow built with n8n, a no-code/low-code automation platform that brings together AI, messaging apps, and social platforms to surface actionable insights from the Nostr social network.
   
   At the heart of this system is a clever orchestration of data flow from Nostr to powerful AI analysis engines and then into user-friendly reports that are delivered seamlessly via Gmail and Telegram.
   
   Let’s explore how this workflow operates and how it transforms raw community content into intelligent, structured reporting.
   
   🧩 Step-by-Step Automation Breakdown
   
   This n8n workflow is triggered either manually or on a schedule. It performs these core tasks:
   
   1. ✨ Data Collection:  
      The “Nostr Read #damus” node fetches recent threads on Nostr tagged with #damus. Damus is a decentralized social media interface for Nostr, often seen as “Twitter over Nostr.” The automation captures posts that mention or discuss Damus-related activity within this decentralized ecosystem.
   
   2. ✅ Content Aggregation:  
      The workflow uses an “Aggregate #damus Content” node to consolidate the fetched posts into a single content block for deeper analysis.
   
   3. 🧠 AI Analysis via Google Gemini:  
      Three AI tasks kick in via the Gemini 2.0 Flash Lite model:
      
      - Theme Detection (#damus Thread Themes): Google Gemini identifies threads' primary themes and recurring narratives.
      - Summarization and Topic Reporting (#damus Themes & Threads Report): The AI generates a detailed report based on those themes, compiling community sentiment, shared motivations, key quotes, and suggestions for improvement.
      - Theme Listing (#damus Themes List): A separate query extracts discrete topics for structured indexing.
   
   4. 🧾 HTML Conversion:  
      To ensure that the data is well-formatted for email and messaging delivery, Markdown output from Gemini is converted to HTML using the “Get HTML” and “Get HTML Report” nodes.
   
   5. ✉️ Email Report via Gmail:  
      The processed summaries and themed analysis are emailed using Gmail integration. This allows team members, analysts, or clients to receive automated, up-to-date insights directly to their inboxes.
   
   6. 📩 Telegram Delivery:  
      For real-time updates and community sharing, the same insights are sent as Telegram messages using the Telegram Bot API. Whether it’s just the themes or the full report, the messages are designed to deliver maximum value within Telegram’s character and format constraints.
   
   🧠 What Makes This Workflow Special?
   
   - Full-Scale AI Utilization:  
     Through LangChain-integrated Google Gemini models, this workflow pushes AI beyond simple summarization. It performs sentiment detection, quote extraction, and even suggested action points.
   
   - Multi-Channel Reporting:  
     Instead of limiting insight distribution to one channel, it delivers to both Gmail users and Telegram subscribers.
   
   - Reusability and Modularity:  
     Various sticky notes in the n8n workflow layout segment the steps for readability and reusability — valuable for collaboration and debugging.
   
   - Context-Aware Reporting:  
     The directions provided to Gemini are detailed, ensuring the response is not generic but tailormade for analyzing decentralized discourse in Nostr's own linguistic and cultural context.
   
   🌍 Use Cases
   
   Whether you’re a community manager monitoring sentiment, a product team at Damus collecting feedback, or a researcher tracking decentralized social trends, this workflow automates hours of manual analysis. It offers a window into grassroots community discussions and provides structured insight into user experiences, challenges, and feature suggestions.
   
   🔗 Conclusions
   
   This workflow is an excellent demonstration of how modern automation and AI can scale human understanding in decentralized environments like Nostr. With a mix of content collection, intelligent processing, and multi-platform delivery, it empowers any stakeholder to extract value from internet chatter without technical expertise.
   
   By combining tools like n8n, LangChain, and Google Gemini, connected via APIs from Gmail and Telegram, the solution becomes greater than the sum of its parts — an intelligent, autonomous reporting system built for the decentralized web.
   
   If you're looking to harness the collective intelligence of your community using smart automation and AI, look no further — this open-source, modular n8n workflow sets the blueprint.
   
   —  
   Written by your AI automation assistant 💡

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.