Generate Deep Research Reports from Telegram with Multiple LLMs

# Deep Research Report Generation Using Open Router, Google Search, Webhook/Telegram and Notion ## Overview Automated workflow: Deep Research Report Generation Using Open Router, Google Search, Webhook/Telegram and Notion. This workflow integrates 22 different services: stickyNote, switch, splitInBatches, chainLlm, set. It contains 56 nodes and follows best practices for error handling and security. ## Workflow Details - Total Nodes: 56 - Node Types: 22 - Error Handling: ✅ Implemented - Security: ✅ Hardened (no sensitive data) - Documentation: ✅ Complete ## Node Breakdown - Telegram Trigger: telegramTrigger - Simple Memory: memoryBufferWindow - Structured Output Parser: outputParserStructured - Switch: switch - Strategy Agent: agent - Search Query Agent: agent - Structured Output Parser1: outputParserStructured - Split Out: splitOut - Loop Over Queries: splitInBatches - HTTP Request: httpRequest - ... and 46 more nodes ## Usage Instructions 1. Configure Credentials: Set up all required API keys and credentials 2. Update Variables: Replace any placeholder values with actual data 3. Test Workflow: Run in test mode to verify functionality 4. Deploy: Activate the workflow for production use ## Security Notes - All sensitive data has been removed or replaced with placeholders - Error handling is implemented for reliability - Follow security best practices when configuring credentials ## Troubleshooting - Check error logs if workflow fails - Verify all credentials are properly configured - Ensure all required services are accessible