AI Moderasi Komentar YouTube Otomatis dengan Multi-Agent LLM (AutoGen + Ollama)

This video demonstrates building an automated YouTube comment moderation system using multi-agent LLM architecture with AutoGen and Ollama. The system leverages multiple specialized AI agents working together to analyze, classify, and moderate comments at scale. It covers the design principles, implementation details, and integration of local LLMs for privacy-preserving content moderation without relying on external APIs.

Key Points

• •Multi-agent architecture enables specialized agents to handle different moderation tasks (classification, toxicity detection, context analysis) in parallel
• •AutoGen framework orchestrates agent communication and task delegation for efficient workflow management
• •Ollama provides local LLM inference, ensuring data privacy and reducing API costs compared to cloud-based solutions
• •Agent roles include: content classifier, toxicity detector, context analyzer, and decision maker for final moderation verdict
• •System can process high-volume comment streams by distributing workload across multiple agents
• •Integration with YouTube API enables real-time comment fetching and automated moderation actions
• •Local LLM deployment reduces latency and allows customization of moderation policies without external dependencies
• •Multi-agent consensus mechanism improves accuracy by combining multiple AI perspectives on content safety

Workflow Diagram

Concepts