Automated Video Pipeline

Generate videos programmatically from data — in CI/CD, on a schedule, or triggered by events.

The Problem

You need to generate the same type of video repeatedly with different data — weekly reports, personalized onboarding videos, per-customer dashboards, changelog announcements. Doing this manually doesn’t scale.

How It Works

Data event → Template composition + injected data → Hyperframes render → Distribute

Hyperframes compositions are just HTML files. You can template them, inject data, and render programmatically — no browser, no human, no AI agent in the loop.

Build It

1. Create a template composition

   Build one great composition with your AI agent, then extract the variable parts:

   <!-- Template: weekly-report/index.html -->
   <div id="root" data-composition-id="main" data-start="0"
        data-duration="15" data-width="1920" data-height="1080">
   
     <div id="metric-1" class="clip" data-start="1" data-duration="4"
          data-track-index="2">
       <span class="value">{{ACTIVE_USERS}}</span>
       <span class="label">active users this week</span>
     </div>
   
     <div id="metric-2" class="clip" data-start="5" data-duration="4"
          data-track-index="2">
       <span class="value">{{REVENUE}}</span>
       <span class="label">revenue</span>
     </div>
   
     <!-- ... more metrics ... -->
   </div>
   
2. Build the generation script

   import subprocess
   import shutil
   from pathlib import Path
   
   def generate_report_video(data: dict, output_path: str):
       """Generate a weekly report video from data."""
   
       # Copy template
       work_dir = Path(f"/tmp/report-{data['week']}")
       shutil.copytree("templates/weekly-report", work_dir, dirs_exist_ok=True)
   
       # Inject data into template
       html = (work_dir / "index.html").read_text()
       html = html.replace("{{ACTIVE_USERS}}", f"{data['active_users']:,}")
       html = html.replace("{{REVENUE}}", f"${data['revenue']:,.0f}")
       html = html.replace("{{GROWTH}}", f"{data['growth_pct']:.1f}%")
       (work_dir / "index.html").write_text(html)
   
       # Render
       subprocess.run([
           "npx", "hyperframes", "render",
           "--output", output_path,
           "--quality", "standard",
           "--fps", "30",
       ], cwd=str(work_dir), check=True)
   
       # Cleanup
       shutil.rmtree(work_dir)
       return output_path
   
3. Trigger from your pipeline

   GitHub Actions:

   # .github/workflows/weekly-report.yml
   name: Weekly Report Video
   on:
     schedule:
       - cron: '0 9 * * 1'  # Every Monday at 9am
   
   jobs:
     generate:
       runs-on: ubuntu-latest
       steps:
         - uses: actions/checkout@v4
         - uses: actions/setup-node@v4
           with:
             node-version: '22'
         - run: sudo apt-get install -y ffmpeg
         - run: python scripts/generate_report.py
         - uses: actions/upload-artifact@v4
           with:
             name: weekly-report
             path: renders/*.mp4
   

   Webhook-triggered:

   from flask import Flask, request
   
   app = Flask(__name__)
   
   @app.route("/webhook/new-signup", methods=["POST"])
   def on_new_signup():
       user = request.json
       generate_welcome_video(
           name=user["name"],
           company=user["company"],
           output=f"renders/welcome-{user['id']}.mp4"
       )
       # Upload to CDN, send via email, etc.
       return {"status": "ok"}
   

Pipeline Patterns

Trigger	Data source	Output	Example
Cron schedule	Database query	Weekly/monthly report video	Monday metrics recap
Webhook	Event payload	Per-user personalized video	Welcome onboarding
Git push	Changelog / commit log	Release announcement	”What’s new in v2.4”
API call	Request parameters	On-demand custom video	Customer dashboard export

Combine with Video Agent

For the best of both worlds — motion graphics + avatar narration:

def generate_narrated_report(data):
    # Step 1: Render the motion graphics with Hyperframes
    graphics_path = generate_report_video(data, "renders/graphics.mp4")

    # Step 2: Generate avatar narration with Video Agent
    narration = requests.post(
        "https://api.heygen.com/v3/video-agents",
        headers={"X-Api-Key": HEYGEN_API_KEY},
        json={
            "prompt": f"""Narrate this weekly report: {data['active_users']:,} active
            users (up {data['growth_pct']:.0f}%), ${data['revenue']:,.0f} revenue.
            Keep it under 15 seconds, upbeat and concise.""",
        },
    ).json()

    # Step 3: Composite in Hyperframes (avatar PiP over graphics)
    # ... or use ffmpeg to overlay

Start simple — get one template working end-to-end, then add automation. A working pipeline that generates one video type reliably is more valuable than a complex system that handles everything.

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Next Steps

Data Visualization

Build the animated visualizations that feed into your pipeline.

Docs to Video

Similar automation pattern using Video Agent for avatar-based content.