Automated Video Pipeline

Build end-to-end automated video pipelines - data in, finished video out. HeyGen API integrates with your CMS, CRM, or data warehouse to render videos on demand.

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.

---

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.