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
- 261 videos deployed across 10 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 2-3 uploads per week (sustained) 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.
