heygen-com/hyperframes

Write HTML. Render video. Built for agents.

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Summary Information

Updated 17 minutes ago
Added to GitGenius on May 18th, 2026
Created on March 10th, 2026
Open Issues & Pull Requests: 248 (-2)
Number of forks: 4,013
Total Stargazers: 41,871 (+9)
Total Subscribers: 120 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 1.3 hours
Mean response time: 15.2 hours
90th percentile: 30.6 hours
Tracked items: 275

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 81% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 15% of issues opened in the past year have been closed. Three people close 94% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 37
New in 7 days: 23
Closed in 7 days: 11
Avg open age: 10 days
Stale 30+ days: 5
Stale 90+ days: 0

Recent activity

Opened in 7 days: 15
Closed in 7 days: 6
Comments in 7 days: 6
Events in 7 days: 16

Top labels

  • bug (66)
  • enhancement (48)
  • good first issue (1)

Detailed Description

HyperFrames is an open-source TypeScript framework developed by HeyGen that converts HTML, CSS, media, and seekable animations into deterministic MP4 videos. The project is designed with a specific focus on enabling AI coding agents to generate video content programmatically, though it also supports local CLI usage and integration into hosted authoring workflows. The framework uses FFmpeg for video rendering and requires Node.js 22 or higher.

The core value proposition centers on a production loop where developers write HTML and CSS, define seekable animations, incorporate media assets, and render them into video output. The framework emphasizes determinism, meaning the same input consistently produces identical video output, which is critical for agent-driven workflows. HyperFrames ships with 21 skills that agents can load on demand, covering everything from a router skill that maps user intent to appropriate workflows, through specialized creation workflows like product launch videos, website-to-video conversion, faceless explainers, and music-synchronized videos, down to atomic domain skills covering animation, keyframes, media handling, and CLI operations.

The repository overlaps with contributors from nousresearch/hermes-agent, openclaw/openclaw, and gatsbyjs/gatsby, suggesting cross-pollination with agent frameworks and web technologies. The primary issue labels tracked by GitGenius reveal a focus on stability and improvement, with bug fixes and enhancements dominating the development agenda.

HyperFrames introduces frame.md, a design system translation layer that adapts brand design specifications from web contexts into video-ready formats. This allows AI agents to compose promotional videos without manual scaling adjustments or web chrome considerations. The framework supports diverse output types including product announcements, pull request walkthroughs with animated code diffs, data visualizations and chart races, social videos with kinetic captions, documentation-to-video conversions, and reusable motion graphics for automated content pipelines.

The skills system teaches agents the complete production workflow: planning the video structure, writing valid HTML, configuring seekable animations, adding media assets, linting for errors, previewing results, and rendering final output. The framework integrates with multiple AI coding agents including Claude Code, Cursor, Gemini CLI, and Codex through a standardized skills interface. Domain-specific skills cover composition contracts with timing attributes, animation across multiple runtimes including GSAP, Lottie, Three.js, and CSS animations, keyframe authoring with seek-safety guarantees, creative direction elements, and media resolution from the HeyGen catalog. The CLI provides development tools including initialization, linting, validation, inspection, preview, rendering, and cloud rendering via AWS Lambda deployment.