Introduction & Installation

WaSHI (WebAssembly-backed, Solver-independent, High-level modeling Interface) brings the algebraic modeling style of Python's PuLP to the TypeScript and JavaScript ecosystem. Write expressions naturally with tagged templates (expr) and solve mathematical optimization problems directly in Node.js and the browser using WebAssembly solvers (HiGHS, Z3, and GLPK).

import { Model, expr, maximize } from "@ohtaman/washi";

const m = new Model("production");

// 1. Define decision variables with realistic finite bounds (lb / ub)
const desks = m.integer("desks", { lb: 0, ub: 100 });
const chairs = m.integer("chairs", { lb: 0, ub: 100 });

// 2. Set the objective function and constraints
m.objective(maximize(expr`30 * ${desks} + 50 * ${chairs}`));
m.constraint(expr`${desks} + 2 * ${chairs} <= 40`, "wood_capacity");
m.constraint(expr`2 * ${desks} + ${chairs} <= 50`, "labor_capacity");

// 3. Solve (built-in HiGHS WebAssembly) and validate the solution
const status = await m.solve();
if (m.hasPrimalSolution) {
  const report = m.validateSolution();
  console.log(status, report.valid); // "Optimal", true
  console.log(`Profit: ${m.objectiveValue} (desks=${desks.value}, chairs=${chairs.value})`); // 1100 (desks=20, chairs=10)
}

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📦 Installation

The default HiGHS solver (LP / MIP / Convex QP) is bundled and works out of the box:

npm

npm install @ohtaman/washi

pnpm

pnpm add @ohtaman/washi

yarn

yarn add @ohtaman/washi

bun

bun add @ohtaman/washi

Install optional peer dependencies only if you need Z3 (SMT logic & Unsat Core diagnosis) or GLPK:

npm install z3-solver   # For SMT logic and Unsat Core conflict diagnosis
npm install glpk.js     # For GLPK solver (GPL-3.0)

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💡 Why TypeScript & WebAssembly?

• Zero native setup across Node.js and the browser: No platform-specific C/C++ builds or separate Python runtimes to manage. A single npm install runs the same modeling code across Node.js APIs, CLIs, and scripts as well as in the browser.
• Lightweight embedding with standard Web APIs & UIs: Go beyond local analysis scripts by shipping interactive Web tools, mini-games, or Chrome extensions that anyone can open via a URL with zero backend hosting cost. You can wire mathematical optimization directly into drag-and-drop UIs or standard browser capabilities—such as screen/camera capture and speech recognition (Web Speech API)—with no network round-trips or external data transfer.
• End-to-end type safety from app data to model: Prevent common indexing and dictionary-key mistakes with TypeScript's type system. Typed variable dictionaries (Record<K, Var>) and variable-reference interpolation (expr`30 * ${qty.desk} <= 40`) connect directly to your application schemas, preserving compile-time variable/key checks, IDE autocompletion, and symbol renaming.
• Strong synergy with AI development & LLM workflows: Keeping your application and optimization model in a single TypeScript codebase lets you validate everything in one test runner—without crossing a Python service boundary—and deploy easily as static assets or a single service. That tight test-and-deploy loop fits AI-assisted coding naturally, while at runtime you can pair flexible LLM extraction with exact solver feasibility and conflict diagnosis (unsatCore) in the same runtime.

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🔍 Core Workflow Essentials

Three practical habits cover the essentials shown in the snippet above:

1. Set realistic finite bounds (lb, ub): When defining variables with m.continuous, m.integer, or m.binary, finite bounds strengthen MIP presolve and enable automatic tight Big-M derivation for logical constraints.
2. Formulate objectives and constraints with expr: Use m.objective(maximize(expr`...`)) and m.constraint(expr`... <= ...`, "name"). Naming constraints lets you pinpoint conflicting rules immediately if a model becomes infeasible.
3. Check hasPrimalSolution and call validateSolution(): After await m.solve(), check m.hasPrimalSolution before reading .value on variables, and call m.validateSolution() to independently verify that bounds, integrality, and constraints hold within tolerance.

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🧭 Next Steps

• Modeling Guide: Array/dictionary variables, aggregations (sum, dot), logical constraints, and Unsat Core diagnosis
• Examples & API Finder: 11 runnable optimization and SMT recipes—from shift scheduling and portfolio QP to vehicle routing
• Solvers Overview: Compare built-in HiGHS with optional Z3 and GLPK adapters
• Runtime Environments: Setup details for Node.js, Vite / Next.js bundlers, CDNs, and static hosting