Skip to main content

nanoframes

SVG-first, browserless, deterministic frame rendering on ThorVG.

nanoframes is an offline counterpart to hyperframes: the same idea — write a frame, render a video, built for agents — but without a browser. Compositions are authored as SVG documents plus a small declarative animation timeline, and every frame is rasterized deterministically by ThorVG (thorvg-python) to PNG, then muxed to MP4 with FFmpeg.

Because nothing depends on a browser, an AI agent can render a frame or a whole clip locally in milliseconds and iterate fast: checkpreviewrender.

Status

v1 complete. Full offline pipeline works end-to-end: initcheckrender/previewvideo. SVG compositions + declarative keyframe timeline are rendered deterministically by ThorVG; MP4 export via ffmpeg; visual snapshots guard regressions. Text auto-layout (measured chips, wrap, curve, fit) + a bundled monospace CJK font (Sarasa Mono SC, ligatures stripped) for solid Chinese. nanoframes lottie additionally renders Lottie/Bodymovin JSON scenes (the text-to-lottie deliverable format) to MP4 offline through ThorVG's native Lottie loader. The agent skill ships a motion/design craft reference library (skills/nanoframes/references/, adapted from text-to-lottie, MIT).

Layout

docs/             architecture + composition + lottie-import contracts
examples/         `.nf.svg` compositions + lottie scenes
skills/nanoframes/ SKILL.md + references/ — agent production loop + craft library
nanoframes/       package (model, parser, timeline, bake, render, lint, cli, video, lottie)
tests/            unit + render + snapshot + CLI tests

Docs

  • Architecture — why SVG + ThorVG, the pipeline, scope decisions.
  • Composition — the .nf.svg contract (timing attributes + animation timeline).
  • Text capabilities — measured chips/wrap/curve/fit, bundled CJK font, text_handler escape hatch.
  • Lottie import — render Lottie JSON scenes to MP4 (nanoframes lottie).

docs/ and skills/ also ship inside the pip wheel (nanoframes/docs, nanoframes/skills); running nanoframes with no arguments prints where the docs and the agent skill live — repo checkout or installed package alike.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nanoframes-0.1.2.tar.gz (13.4 MB view details)

Uploaded Source

File details

Details for the file nanoframes-0.1.2.tar.gz.

File metadata

  • Download URL: nanoframes-0.1.2.tar.gz
  • Upload date:
  • Size: 13.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.10

File hashes

Hashes for nanoframes-0.1.2.tar.gz
Algorithm Hash digest
SHA256 e03c40957f6c791e98112c066d322d642f147900d44f97f1e4823fa9101bc8ef
MD5 2630a86a6c0e77a10ba13ed01e956b1b
BLAKE2b-256 ecccac4da353cea9209a08f653a6fc3420ea00e53c9ac5d9b5667d125573ec2f

See more details on using hashes here.

Release history Release notifications | RSS feed

0.2.12

1 file

0.2.11

1 file

0.2.10

1 file

0.2.9

1 file

0.2.0

1 file

0.1.11

1 file

0.1.7

1 file

0.1.6

1 file

0.1.5

1 file

0.1.4

1 file

0.1.3

1 file

This release

0.1.2 This release

1 file

0.1.1

1 file

0.1.0

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page