A multi-agent system that automates video research, scripting, rendering, upload scheduling, and analytics — fully self-hosted. The Problem Running a YouTube channel is a full-time job. Research topics, write scripts, generate thumbnails, render videos, optimize SEO, upload on schedule, analyze performance — and repeat. For a solo developer team, this quickly becomes overwhelming. We built a multi-agent system that handles the entire pipeline autonomously. Architecture: 3 Nodes, 1 Swarm Bus Our system runs on three AI agents connected through a shared message bus: Node Role atlas_core Orchestrator — coordinates tasks, manages memory, handles security audits suckz Content pipeline — video rendering, upload scheduling, YouTube API calls dev Support — SEO optimization, tool research, code fixes, analytics Communication happens through SQLite-backed inbox/outbox. Each node has its own task queue, heartbeat monitoring, and priority-based message delivery. The Pipeline 1. Topic Research Agents scan tech trends, community discussions, and competitor channels. Each topic gets fact-verified and tagged with keywords before entering the pipeline. 2. Script Generation AI-powered scripts follow a strict blueprint: Hook (first line = YouTube title) Content (250-300 words) Engagement (call to action) Anti-CTA ("Do not subscribe") Tactical Debrief (3-4 learning bullets) Exploit Timeline (chapter markers) 3. Video Rendering Piper TTS (offline, free) generates voiceover. FFmpeg handles rendering with PIL-piped terminal backgrounds, Karaoke ASS subtitles, and glitch overlays. No cloud APIs needed. 4. SEO Optimization Every video gets: Blueprint-compliant description (validated by a gate function) Hashtags (max 5, at the bottom) Chapter timestamps Title A/B testing 5. Upload Scheduling The deploy script handles: Resumable YouTube API uploads Quota tracking (100 uploads/day + 10k units/day limit) Scheduled publishing (publishAt timestamps) Pre-upload quality gates (metadata, description length, blueprint compliance) Post-upload verification (live metadata check) 6. Analytics Bulk Reporting API jobs pull daily: Watch time Impressions + CTR Traffic sources Per-video performance Results feed back into topic selection and title optimization. Key Metrics 250+ videos deployed across 7 waves Zero cloud APIs for TTS and rendering (100% offline) Automated quality gates catch 95% of metadata issues before upload 48-hour analytics loop — data-driven topic selection Lessons Learned 1. Upload Cannibalization Kills CTR Posting 14 videos/day crushed our click-through rate. We found that 2 uploads/day with 6+ hour gaps performs dramatically better. 2. Blueprints Beat Free-Form Strict content structure (hook → content → engagement → CTA → debrief) consistently outperforms free-form scripts. 3. Offline First Piper TTS + FFmpeg gives us unlimited renders at zero cost. Cloud TTS APIs add latency and cost without meaningful quality improvement. 4. Memory Matters Cross-session memory (we use MemPalace with knowledge graphs) prevents repeated mistakes and preserves decisions across agent restarts. Stack Agents: Custom Python swarm with SQLite message bus TTS: Piper (offline, CPU-only) Video: FFmpeg + Ken Burns effects YouTube API: Data API v3 with quota tracking Analytics: YouTube Reporting API (bulk CSV reports) Memory: MemPalace (semantic search + knowledge graph) Config: OpenCode with MCP integrations What's Next Thumbnail A/B testing with CTR scoring tools Automated dev.to cross-posting Expand to multi-platform (TikTok, Rumble) Built by the ERR.SYS / 0xRAGE404 team. Find us at youtube.com/@0xRAGE.404 .