TrendRadar is an AI-driven public opinion and trend monitoring tool that aggregates trending topics from multiple social platforms, provides AI-powered analysis, and delivers intelligent alerts across 9+ notification channels. The project has reached 47,100 GitHub stars and 22,300 forks, with its latest v6.0.0 release introducing a unified scheduling system and visual configuration editor. The tool bridges the gap between raw social media data and actionable intelligence by combining multi-platform aggregation with LLM-powered sentiment analysis, trend prediction, and multilingual translation. It supports GPT, DeepSeek, Gemini, and other models through LiteLLM integration, and its MCP (Model Context Protocol) v4.0.0 support enables AI agent integration for automated workflows. ## Multi-Platform Aggregation Engine TrendRadar collects trending content from multiple social platforms simultaneously, along with custom RSS feed subscriptions that support keyword filtering. The aggregation layer normalizes data from different sources into a unified format, enabling cross-platform trend comparison and correlation analysis. Users can track specific topics across platforms to identify emerging narratives before they go mainstream. ## Five-Section Content Architecture The v6.0.0 release introduces a structured content delivery system that separates trending news, RSS feeds, new updates, standalone displays, and AI analysis into distinct notification blocks. This architecture prevents information overload by letting users subscribe only to the content categories relevant to their monitoring needs. Each section can have independent AI summaries and analysis. ## Unified Scheduling with Timeline YAML The scheduling system uses a `timeline.yaml` configuration with 5 preset templates for different monitoring cadences. A visual configuration editor lets non-technical users set up monitoring schedules without editing YAML directly. This makes TrendRadar accessible to marketing teams, journalists, and analysts who need automated trend monitoring without DevOps overhead. ## Smart Notification Delivery Alerts are delivered across WeChat, Telegram, Dingtalk, Feishu, email, Slack, and 3+ additional channels. The MCP v4.0.0 integration enables intelligent batch sending with platform-specific byte limits and format guide generation, ensuring optimal readability on each channel. AI-powered message distribution automatically adapts content length and formatting to each platform's constraints. ## Deployment Flexibility TrendRadar supports Docker container deployment with local and cloud data persistence, GitHub Actions automation for scheduled runs, and one-click fork-and-deploy for rapid setup. Setup scripts are provided for both macOS and Windows environments, and the project includes documentation in Chinese and English.