* feat(gcp-cloud-run): add Google Cloud Run + Workflows distributed render adapter Adds @hyperframes/gcp-cloud-run, the GCP counterpart to @hyperframes/aws-lambda (issue #932). The OSS distributed primitives (plan, renderChunk x N, assemble) are unchanged; this package is the storage/compute/orchestration glue. Package: Cloud Run handler (one image, three actions), runs under bun; GCS transport; in-image chrome-headless-shell resolver; client SDK (renderToCloudRun, getRenderProgress, deploySite, computeRenderCost); Dockerfile; Cloud Workflows definition; Terraform module; CLI cloudrun deploy|sites|render|render-batch|progress|destroy with --output-resolution and --strict-variables; 62 unit tests + docs + live smoke script. Shared extraction (removes ~640 lines of adapter duplication): move the cloud-agnostic config validator + content-hash into producer/distributed; both adapters import them. Validated end-to-end on GCP at 37.4 dB PSNR vs baseline. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(cli): resolve @hyperframes/gcp-cloud-run in the CLI build + root build The CLI bundle (esbuild) couldn't resolve `@hyperframes/gcp-cloud-run/sdk`, failing Build/Typecheck/CLI-smoke (and the perf/windows/regression jobs that build first). Mirror the aws-lambda handling: mark the gcp adapter + its /sdk subpath external in tsup.config.ts with a source alias, and add gcp-cloud-run to the root `build` filter so its dist exists for publish + runtime. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(ci): copy gcp-cloud-run manifest in Dockerfile.test for frozen install The regression test image runs `bun install --frozen-lockfile` after copying each workspace package.json individually. The CLI now depends on @hyperframes/gcp-cloud-run (workspace:*), so the frozen install fails to resolve it unless its manifest is present. Add the COPY line. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * feat(cli): add machine-sizing flags to `cloudrun deploy` Closes the parity gap with `lambda deploy` (which exposes --memory etc.). `cloudrun deploy` now threads --cpu, --memory, --max-instances, and --timeout into the Terraform apply; omitted flags keep the module defaults (4 vCPU / 16Gi / 100 instances / 3600s). For finer control, apply the module directly. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(gcp-cloud-run): address PR review (security, waste, limits, alerts) - server.ts: bucket-allowlist guard no longer fails open silently. Unset env logs a one-time WARNING; "*" is an explicit opt-out; otherwise it enforces. - server.ts: stop double-shipping audio.aac. It already rides in the plan tarball every consumer downloads, so drop the redundant standalone upload (plan) + re-download/overwrite (assemble); assemble reads it from the untar, falling back to a supplied AudioGcsUri for compat. - server.ts: chunk extension via path.extname() instead of slice(lastIndexOf). - workflow.yaml: clamp parallel concurrency_limit to math.min(chunkCount, 20) — Cloud Workflows hard-caps concurrent iterations at 20. - Dockerfile: pin bun (bun-v1.3.9) so an interop change can't silently break the image rebuild. - terraform: add min_instances var (default 0); add a workflow-failure alert (finished_execution_count status=FAILED) alongside the request-count one. - costAccounting: document that displayCost excludes GCS storage/egress. Verified against the actual APIs: @google-cloud/workflows@4.4.0 ICreateExecutionRequest has no executionId (so the idempotency-token suggestion isn't available in this client); Workflows concurrency cap is 20; failure metric is workflows.googleapis.com/finished_execution_count (status label). 174 adapter tests pass, fallow/oxlint/oxfmt/terraform clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(gcp-cloud-run): address round-2 review — error code + CFR forwarding - workflow.yaml: rename the zero-chunk failure code PLAN_TOO_LARGE → PLAN_PRODUCED_ZERO_CHUNKS. The old code implied a size-ceiling breach (the opposite cause), misleading anyone triaging the alert. - workflow.yaml: forward Config.cfr to the assemble step (`Cfr: ${("cfr" in config) and config.cfr}`). It was read by the handler but never sent, so exact-CFR was silently off for every Cloud Run render. Uses the same `in`-operator guard already proven in the retryable predicate. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * fix(release): include gcp-cloud-run in set-version PACKAGES list set-version.ts (driven by release:prepare) bumps an explicit package list to the shared version on each release. gcp-cloud-run was wired into the build + publish.yml but missing here, so a release would leave it at a stale version and publish.yml would push the wrong version. Add it so the new package version-bumps + publishes in lockstep with the others. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Write HTML. Render video. Built for agents.
Quickstart | Showcase | Playground | Catalog | Docs | Discord
HyperFrames is an open-source framework for turning HTML, CSS, media, and seekable animations into deterministic MP4 videos. Use it locally with the CLI, from AI coding agents with skills, or as the rendering core behind hosted authoring workflows.
Quick Start
With an AI coding agent
Install the HyperFrames skills, then describe the video you want:
npx skills add heygen-com/hyperframes
Try a prompt like:
Using
/hyperframes, create a 10-second product intro with a fade-in title, a background video, and subtle background music.
The skills teach agents the HyperFrames production loop: plan the video, write valid HTML, wire seekable animations, add media, lint, preview, and render. They work with Claude Code, Cursor, Gemini CLI, Codex, and other coding agents that support skills.
For visual design handoff workflows, see the Claude Design guide and Open Design guide.
Manually with the CLI
npx hyperframes init my-video
cd my-video
npx hyperframes preview # preview in browser with live reload
npx hyperframes render # render to MP4
Requirements: Node.js 22+, FFmpeg
What You Can Build
Need ideas? Browse the Showcase for finished videos you can watch, read, run, and remix.
- Product launch videos and feature announcements
- PR walkthroughs with animated code diffs, narration, and captions
- Data visualizations, chart races, and map animations
- Social videos with kinetic captions, overlays, and music
- Docs-to-video, PDF-to-video, and website-to-video explainers
- Reusable motion graphics for automated content pipelines
Frame.md
frame.md — your design system, ready for video.
Every brand has a design.md. None of them were written for a camera. frame.md is the missing translation layer: it takes your web-context design spec and inverts it for the frame — the same tokens, the same rules, but rewritten so an AI agent can compose a promo video without guessing at scale or reaching for web chrome.
The output is a DESIGN.md superset your whole toolchain can read. Atoms stay sacred. Composition stays free. Numbers come from the script.
Biennale Yellow |
BlockFrame |
Blue Professional |
Bold Poster |
Broadside |
Capsule |
Cartesian |
Cobalt Grid |
Coral |
Creative Mode |
Browse and remix them all at hyperframes.dev/design.
How It Works
Define a video as HTML. Add data attributes for timing and tracks. Use GSAP, CSS, Lottie, Three.js, Anime.js, WAAPI, or your own frame adapter for seekable animation.
<div id="stage" data-composition-id="launch" data-start="0" data-width="1920" data-height="1080">
<video
class="clip"
data-start="0"
data-duration="6"
data-track-index="0"
src="intro.mp4"
muted
playsinline
></video>
<h1 id="title" class="clip" data-start="1" data-duration="4" data-track-index="1">Launch day</h1>
<audio
data-start="0"
data-duration="6"
data-track-index="2"
data-volume="0.5"
src="music.wav"
></audio>
<script src="https://cdn.jsdelivr.net/npm/gsap@3/dist/gsap.min.js"></script>
<script>
const tl = gsap.timeline({ paused: true });
tl.from("#title", { opacity: 0, y: 40, duration: 0.8 }, 1);
window.__timelines = window.__timelines || {};
window.__timelines.launch = tl;
</script>
</div>
Preview instantly in the browser. Render locally or in Docker. The renderer seeks each frame in headless Chrome and encodes the result with FFmpeg, so the same input produces the same video.
HyperFrames Stack
HyperFrames is the open-source rendering engine, plus a growing set of tools around HTML-native video creation.
| Piece | Status | What it does |
|---|---|---|
| CLI | Available | Scaffold, preview, lint, inspect, and render local video projects |
| Core / Engine / Producer | Available | Parse compositions, drive headless Chrome, encode video, and mix audio |
| Catalog | Available | Reusable blocks and components for transitions, overlays, captions, charts, maps, and effects |
| Agent skills | Available | Teach coding agents the video-production patterns that generic web docs miss |
| Studio | Available, evolving | Browser surface for previewing and editing compositions |
| AWS Lambda rendering | Available | Deploy a distributed render stack and drive renders from your laptop or CI |
| hyperframes.dev | Available | Community playground for previewing, iterating, sharing, and rendering HTML-native video projects |
| frame.md | Available | Invert your design system for the camera — a DESIGN.md superset an agent can compose video from |
Catalog
Install ready-to-use blocks and components:
npx hyperframes add flash-through-white # shader transition
npx hyperframes add instagram-follow # social overlay
npx hyperframes add data-chart # animated chart
Browse the catalog at hyperframes.heygen.com/catalog.
Why HyperFrames?
- HTML-native: compositions are HTML files with data attributes. No React requirement, no proprietary timeline format.
- Agent-friendly: agents already write HTML, and the CLI is non-interactive by default.
- Deterministic: same input, same frames, same output. Built for CI, regression tests, and automated rendering.
- No build step: an
index.htmlcomposition plays as-is and can be previewed directly in the browser. - Adapter-based animation: bring GSAP, CSS animations, Lottie, Three.js, Anime.js, WAAPI, or a custom runtime.
- Open source: Apache 2.0 license, with no per-render fees or commercial-use thresholds.
HyperFrames vs Remotion
HyperFrames is inspired by Remotion. Both tools render video with headless Chrome and FFmpeg. The main difference is the authoring model: Remotion's bet is React components; HyperFrames' bet is plain HTML that humans and agents can both write easily.
| HyperFrames | Remotion | |
|---|---|---|
| Authoring | HTML + CSS + seekable animation | React components |
| Build step | None; index.html plays as-is |
Bundler required |
| Agent handoff | Plain HTML files | JSX / React project |
| Library-clock animations | Seekable, frame-accurate via adapters | Wall-clock animation patterns need care |
| Distributed rendering | Local and AWS Lambda render paths | Remotion Lambda, mature cloud renderer |
| License | Apache 2.0 | Source-available Remotion License |
Read the full comparison in the HyperFrames vs Remotion guide.
Documentation
Full documentation: hyperframes.heygen.com/introduction
Packages
| Package | Description |
|---|---|
hyperframes |
CLI for creating, previewing, linting, and rendering compositions |
@hyperframes/core |
Types, parsers, generators, linter, runtime, and frame adapters |
@hyperframes/engine |
Seekable page-to-video capture engine using Puppeteer and FFmpeg |
@hyperframes/producer |
Full rendering pipeline for capture, encode, and audio mix |
@hyperframes/studio |
Browser-based composition editor UI |
@hyperframes/player |
Embeddable <hyperframes-player> web component |
@hyperframes/shader-transitions |
WebGL shader transitions for compositions |
@hyperframes/aws-lambda |
AWS Lambda SDK and deployment surface for distributed renders |
Community
HyperFrames is used in production at HeyGen, with community examples from teams like tldraw, TanStack, and others in ADOPTERS.md. Open a PR if your team is using HyperFrames.
- Questions and ideas: Discord
- Bugs and feature requests: GitHub Issues
- Security reports: SECURITY.md
- Contributions: CONTRIBUTING.md
Development Note
The repo uses Git LFS for golden regression-test baselines under packages/producer/tests/**/output.mp4 (about 240 MB of .mp4 files). If you're cloning the full repo for development, install Git LFS first:
# macOS
brew install git-lfs
# Ubuntu / Debian
sudo apt install git-lfs
# Windows
winget install GitHub.GitLFS
# Then, once per machine
git lfs install
If you only need source files, you can skip LFS content:
GIT_LFS_SKIP_SMUDGE=1 git clone https://github.com/heygen-com/hyperframes.git










