This is our editors' assessment out of 5, not an average of user ratings. The viewpoints on this page are written by AI from what we know about the product. They are not customer reviews.
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).
Super clean way to run tensors in TypeScript without leaving for Python.
Bun integration makes it blazing fast.
Great experimental research tool from Meta.
Documentation could be deeper, but the code rocks.
AI-synthesized perspectives grounded in live site inspection, pricing structure, and category benchmarks — presented as editorial viewpoints rather than individual customer testimonials.
Create tensors and track gradients with requires_grad and backward(), enabling automatic differentiation in plain JavaScript and TypeScript.
Automatically targets an attached GPU or accelerator, using CUDA on Linux and a CPU backend on macOS through ArrayFire.
Leverages the Bun runtime and the Flashlight C++ deep-learning framework for native performance inside a JavaScript workflow.
Offers standard utilities like randn, identity, matmul, scalar operations, and shape inspection that mirror popular numeric libraries.
Converts effortlessly between JavaScript typed arrays such as Float32Array and Shumai tensors in both directions.
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.
Experiment with gradients and small neural networks without leaving the JS/TS ecosystem, ideal for research prototypes and proofs of concept.
Bring tensor math and differentiable operations into Bun-powered server or tooling projects that need on-device computation.
Walk through backpropagation and matrix operations in a readable, web-native language with an approachable open-source codebase.
Side-by-side telemetry comparison of competing AI tools in the same primary category
| Rank | AI Tool | Monthly Visits | MoM Growth | Direct traffic share | Pricing | Head-to-Head |
|---|---|---|---|---|---|---|
| ● | Shumai | 0 | — | — | Free | Current |
| 1 | MindSpore (昇思) | 46.57K | -34.1% | 42% direct | Free | vs MindSpore (昇思) |
| 2 | Mastra | 318.95K | -5% | 44% direct | Freemium, Contact for Pricing | vs Mastra |
| 3 | Together AI | 1.65M | +64.9% | 54% direct | Freemium, Paid | vs Together AI |
| 4 | Captum | 4.46K | -42.7% | 35% direct | Free | vs Captum |
| 5 | Unsloth AI | 887.46K | -17.6% | 30% direct | Free | vs Unsloth AI |
| 6 | Convex | 390.42K | -18.7% | 65% direct | Freemium, Paid | vs Convex |
Free / Open-sourceHuawei's open-source all-scenario deep learning framework
Free / Open-sourceHuawei's open-source all-scenario deep learning framework
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Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all backed by in-house systems research.
Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all backed by in-house systems research.
Free / Open-sourceOpen-source model interpretability for PyTorch
Free / Open-sourceOpen-source, memory-efficient fine-tuning and local model running
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Free / Open-sourceReactive TypeScript backend with database, functions and AI gateway
Free / Open-sourceReactive TypeScript backend with database, functions and AI gateway
Free / Open-sourceGoogle's family of lightweight, open-weight AI models
Free / Open-sourceGoogle's family of lightweight, open-weight AI models
Give your AI agents secure, authenticated access to 1,700+ apps
Give your AI agents secure, authenticated access to 1,700+ apps
Fast Apply and agent-tuned inference for coding agents
Fast Apply and agent-tuned inference for coding agents
Free / Open-sourceThe portable data lakehouse for AI agents
Free / Open-sourceOne OpenAI-compatible API to route, monitor and control 250+ LLM models
Free / Open-sourceOne OpenAI-compatible API to route, monitor and control 250+ LLM models
Run, fine-tune, and deploy machine learning models with one line of code
Run, fine-tune, and deploy machine learning models with one line of code
