# Shumai

According to OpenAITools.app telemetry verified on 2026-11-11, Shumai (github.com) is an AI platform indexed under AI Developer Tools generating 0 monthly visits with a month-over-month traffic delta of +0 (0.0%). Its primary geographic market is Global (— traffic share), and it operates on a Free pricing model. Top competing alternatives by monthly volume include MindSpore (昇思) (46.57K visits/mo), Mastra (318.95K visits/mo), Together AI (1.65M visits/mo).

Shumai — Differentiable tensors in JS/TS

## Key Telemetry & Specifications

| Metric | Value |
| :--- | :--- |
| Tool Name | Shumai |
| Handle | shumai-meta |
| Status | active |
| Origin | official |
| Maintenance | active |
| Official Domain | github.com |
| Website URL | https://github.com/facebookresearch/shumai |
| Categories | AI Developer Tools, AI Research Tool, AI GitHub |
| Pricing Model | Free |
| Monthly Visits | 0 |
| MoM Traffic Change | 0 |
| MoM Growth Rate | — |
| Primary Market | Global |
| Market Share | — |
| Direct Visits | 0 |
| Organic Search Visits | 0 |
| Social Visits | 0 |
| Referral Visits | 0 |
| Email Visits | 0 |
| Review Score | 4.7 |
| Saved Count | 1 |
| Supported Platforms | Open Source |
| Billing Platforms |  |
| Attributes | Pricing: Free; Features: Open Source |
| Tags | Tensor library, JavaScript, TypeScript, Differentiable tensors, Machine learning, Deep learning, GPU acceleration, CUDA, Bun, Flashlight |

## Overview

Shumai is a research-driven open-source library from Facebook Research that ports the power of a modern deep-learning tensor framework into the JavaScript and TypeScript ecosystem. Instead of switching to Python, developers can create tensors, run matrix multiplications, and perform automatic differentiation directly in familiar JS/TS syntax, while the heavy numerical work is delegated to the Flashlight C++ backend and ArrayFire.

Because it is built for Bun and ArrayFire, Shumai automatically attempts to use an attached GPU or accelerator, defaulting to CUDA on Linux and CPU on macOS. The package exposes standard array utilities such as randn, identity, matmul, and shape inspection, plus gradient tracking through requires_grad and backward(), so it can be used to prototype small neural networks, teach machine-learning concepts, or experiment with differentiable programming in a web-native environment.

The project is explicitly labeled experimental software, so APIs may change and installation requires installing Bun and ArrayFire as prerequisites. It is published under the MIT license on GitHub, where the maintainers accept issues and contributions from the community.

## Pricing & Plans

Free and open source under the MIT license. There are no paid tiers, subscriptions, or usage fees.

## Key Features & Capabilities

- **Differentiable tensors in JS/TS:** Create tensors and track gradients with requires_grad and backward(), enabling automatic differentiation in plain JavaScript and TypeScript.
- **GPU acceleration via ArrayFire:** Automatically targets an attached GPU or accelerator, using CUDA on Linux and a CPU backend on macOS through ArrayFire.
- **Built on Bun + Flashlight:** Leverages the Bun runtime and the Flashlight C++ deep-learning framework for native performance inside a JavaScript workflow.
- **Familiar array operations:** Offers standard utilities like randn, identity, matmul, scalar operations, and shape inspection that mirror popular numeric libraries.
- **Native array interop:** Converts effortlessly between JavaScript typed arrays such as Float32Array and Shumai tensors in both directions.

## Primary Use Cases

- **Prototyping models in JavaScript (Machine-learning engineers):** Experiment with gradients and small neural networks without leaving the JS/TS ecosystem, ideal for research prototypes and proofs of concept.
- **Adding numerical computing to apps (Web developers):** Bring tensor math and differentiable operations into Bun-powered server or tooling projects that need on-device computation.
- **Teaching differentiable programming (Educators and students):** Walk through backpropagation and matrix operations in a readable, web-native language with an approachable open-source codebase.

## Strengths

- Actively maintained open-source project from Facebook Research with a permissive MIT license
- Enables GPU-accelerated tensor math directly in TypeScript and JavaScript via Bun and ArrayFire
- Straightforward API that mirrors familiar tensor libraries, with clear install and quickstart docs
- Free to use with no licensing cost for individuals, researchers, or commercial projects

## Limitations

- Explicitly experimental software, so APIs and install steps can change between releases
- Requires additional setup of Bun and ArrayFire, and only macOS and Linux are officially supported
- CPU-only computation relies on the ArrayFire CPU backend, which the project notes is not well-optimized

## Frequently Asked Questions

### What is Shumai?
Shumai is a fast, differentiable tensor library for TypeScript and JavaScript, built on Bun and Flashlight by Facebook Research. It brings GPU-accelerated tensor operations and automatic differentiation to the JS/TS ecosystem.

### How much does Shumai cost?
It is completely free and open source under the MIT license, with no paid plans or usage fees.

### Which platforms are supported?
macOS and Linux are officially supported, with Linux defaulting to CUDA GPU computation and macOS using the CPU backend. Windows is not officially supported, though some users run it through Docker and WSL2.

### Is Shumai production-ready?
Not yet. The maintainers label it experimental software, so expect evolving APIs and potential installation issues; it is best suited to research, prototyping, and experimentation for now.

### What is Shumai's monthly traffic and growth trend?
Shumai currently receives 0 monthly visits, with a month-over-month traffic change of +0 (0.0% growth rate). Its primary source market is Global (— share), including 0 direct visits/mo and 0 organic search visits/mo.

### Is Shumai free? What is its pricing model?
Shumai operates on a Free pricing model. Free and open source under the MIT license. There are no paid tiers, subscriptions, or usage fees. Supported billing and checkout platforms include: not listed.

### What are the best Shumai alternatives in 2026?
Based on OpenAITools.app category traffic and feature fit, the top five alternatives to Shumai are: 1. MindSpore (昇思) (46.57K visits/mo); 2. Mastra (318.95K visits/mo); 3. Together AI (1.65M visits/mo); 4. Captum (4.46K visits/mo); 5. Unsloth AI (887.46K visits/mo).

### Which platforms and core AI capabilities does Shumai support?
Shumai is classified under AI Developer Tools, AI Research Tool, AI GitHub, supports the following platforms: Open Source, and its core capability tags include: Tensor library, JavaScript, TypeScript, Differentiable tensors, Machine learning, Deep learning.

## Alternatives

| Tool Name | Monthly Visits | Pricing Model | Compare |
| :--- | :--- | :--- | :--- |
| [MindSpore (昇思)](https://openaitools.app/tool/mindspore-cn) | 46,573 | Free | [Compare](https://openaitools.app/compare/mindspore-cn-vs-shumai-meta) |
| [Mastra](https://openaitools.app/tool/mastra) | 318,954 | Freemium, Contact for Pricing | [Compare](https://openaitools.app/compare/mastra-vs-shumai-meta) |
| [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | Freemium, Paid | [Compare](https://openaitools.app/compare/shumai-meta-vs-together-ai) |
| [Captum](https://openaitools.app/tool/captum-ai) | 4,464 | Free | [Compare](https://openaitools.app/compare/captum-ai-vs-shumai-meta) |
| [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | Free | [Compare](https://openaitools.app/compare/shumai-meta-vs-unsloth-ai) |
| [Convex](https://openaitools.app/tool/convex) | 390,417 | Freemium, Paid | [Compare](https://openaitools.app/compare/convex-vs-shumai-meta) |
| [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | Freemium, Paid | [Compare](https://openaitools.app/compare/gemma-open-models-by-google-vs-shumai-meta) |
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| [Spice.ai](https://openaitools.app/tool/spice-ai) | 20,632 | Freemium | [Compare](https://openaitools.app/compare/shumai-meta-vs-spice-ai) |
| [LLM Gateway](https://openaitools.app/tool/llm-gateway) | 168,426 | Freemium, Contact for Pricing | [Compare](https://openaitools.app/compare/llm-gateway-vs-shumai-meta) |
| [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | Paid | [Compare](https://openaitools.app/compare/replicate-ai-vs-shumai-meta) |

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Source: https://openaitools.app/tool/shumai-meta