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Expect API changes between versions and bugs.\n\n## Quick start\n\n```\nnpm install longpipe\n```\n\n```ts\nimport { EffectsPipeline } from 'longpipe'\n\nconst stream = await navigator.mediaDevices.getUserMedia({ video: true, audio: true })\n\nconst pipeline = new EffectsPipeline(stream, {\n  background: 'blur',   // or an image, video, or solid color\n  touchup: true,        // skin smoothing\n  reframe: true,        // auto-frame the subject\n  audio: 'denoise',     // mic noise removal\n})\n\nvideoEl.srcObject = pipeline.stream   // available immediately\nawait pipeline.ready                  // optional — resolves once the effect is live\n```\n\n`pipeline.stream` is wired synchronously and emits the unprocessed input until the model is ready (~1–3 s on cold start), so users see live video the whole time. Model weights stream from `cdn.longpipe.dev` by default — no extra setup, and [self-hostable](https://longpipe.dev/docs/self-hosting).\n\n## Why Longpipe\n\n- **One model, many effects.** Backgrounds, touch-up, and auto-reframe all run off a single shared encoder pass — enabling another effect doesn't cost another inference. Audio denoise runs in a parallel `AudioWorklet`, off the GPU entirely. ([Architecture](https://longpipe.dev/docs/architecture))\n- **More accurate *and* faster than the open-source alternatives.** Custom-trained models running as pure WebGPU/WebGL shaders in a zero-copy, fully-GPU pipeline — no general-purpose runtime, no CPU↔GPU round trips.\n- **Works everywhere.** Chromium, Firefox, and Safari (desktop + iOS). WebGPU when available, WebGL2 fallback, per-browser frame transport handled internally.\n- **Adapts to the device.** Five model presets (`xs`–`xl`); autotune benchmarks the actual device at init and an adaptive controller swaps presets at runtime to hold 30 fps — on 10-year-old netbooks and the latest MacBook Pro alike.\n- **Production pedigree.** Built by the founder of [Vectorly](https://www.crunchbase.com/organization/vectorly), a commercial effects SDK acquired in 2021 — a ground-up redesign for the WebGPU era.\n\n### Best models on the web\n\nLongpipe's hydranet architecture, custom model-specific shaders, zero-memory-copy pipeline, developer friendly interface and 100% MIT open source license mean that 🐲 Longpipe pareto dominates on performance, model quality and ease of use.\n\n<img width=\"958\" height=\"652\" alt=\"lp-performance\" src=\"https://github.com/user-attachments/assets/792b9f63-e8fd-4d30-aa90-9c92b6a0430c\" />\n\n\n## Documentation\n\nFull docs live at **[longpipe.dev/docs](https://longpipe.dev/docs)**:\n\n- [Getting started](https://longpipe.dev/docs/getting-started)\n- [API reference](https://longpipe.dev/docs/api) — all `EffectsPipeline` options and methods\n- [Architecture](https://longpipe.dev/docs/architecture) — the hydranet: one encoder, many heads\n- [Backgrounds](https://longpipe.dev/docs/backgrounds) · [Touch-up](https://longpipe.dev/docs/touchup) · [Auto-reframe](https://longpipe.dev/docs/reframe) · [Audio denoise](https://longpipe.dev/docs/audio)\n- [Presets & autotune](https://longpipe.dev/docs/presets)\n- [Model layer](https://longpipe.dev/docs/model-layer) — drive the model directly, without the pipeline\n- [Self-hosting weights](https://longpipe.dev/docs/self-hosting)\n\nMachine-readable for LLMs and agents: [`llms.txt`](https://longpipe.dev/llms.txt) · [`llms-full.txt`](https://longpipe.dev/llms-full.txt)\n\n## Roadmap\n\n- [x] Background segmentation / virtual backgrounds\n- [x] Background noise removal (audio, separate pipeline)\n- [x] Face landmarks + touch-up\n- [x] Multi-face support\n- [x] Auto-reframe\n- [ ] AR effects\n- [ ] Lighting correction\n\n## License\n\nSDK source code **and** pre-trained model weights are MIT-licensed — see [LICENSE](LICENSE) and [WEIGHTS_LICENSE](WEIGHTS_LICENSE). You're free to self-host the weights inside other open-source or commercial projects under MIT-compatible terms.\n\nThe weights were trained on permissively-licensed public datasets (P3M-10k, AISegment, COCO, OpenImages), Z-Image synthetics, and a custom webcam dataset collected with participants' explicit, informed consent. The full dataset list and provenance details: [longpipe.dev/docs/licensing](https://longpipe.dev/docs/licensing).\n","readmeFilename":"README.md"}