Business logic as plain data

Reproduce a production bug on your laptop, no database required. Pure Effect is a zero-dependency effect library for JavaScript and TypeScript. Your business logic returns plain objects describing the I/O it would perform, instead of performing it. You can read those objects in a test or replay them from a failed production run, and the database is never touched until the interpreter runs them. $ npm install pure-effect

01  /  problem It works on your machine. It broke in production. You can't reproduce it. The cause is usually the same: business logic and I/O are tangled together. When you write await db.findUser(email), the call fires immediately, mid-logic. So a test can only check what happened by making the I/O happen too, against a mock, a fake, or a container. And when production fails, all you have is a stack trace, because the calls the request actually made were never captured to replay.

async / await: the I/O is the logic // The call fires immediately, mid-logic. async function registerUser(input) { const found = await db.findUser(input.email); if (found) throw new Error('Email in use'); return db.saveUser(input); }

// To test it you must run it. When it fails // in prod, nothing was recorded to replay. You check behavior by executing it. The failed run leaves no trace you can step through.

pure-effect: the logic returns I/O as data // Read what it would do first. Nothing ran. const flow = registerUserFlow(input); assert.equal(flow.cmd.name, 'cmdFindUser');

// Feed in what production saw and walk the // exact same path, no database is touched. const next = flow.next(recordedUser); assert.equal(next.cmd.name, 'cmdSaveUser'); You check it by reading the tree.

The same calls can be recorded in prod and replayed here, with no infrastructure.

02  /  primitives Six pieces. Learnable in an afternoon. Every Effect is one of these shapes. They compose into trees the interpreter walks at the edge of your system.

i. Success(value) OK A successful computation result. Returns { type: 'Success', value }. Any pipeline step can return one to feed the next. ii. Failure(error) ERR Stops the pipeline immediately and short-circuits remaining steps. Optional initialInput is preserved for diagnostics. iii. Command(cmd, next) deferred I/O A side effect described as data. cmd is the function that would run; next turns its result into the next Effect. iv. Ask(next) context · DI Reads the context object passed to runEffect such as tenant, request id, and config without threading it through every signature. v. Retry(effect, opts) resilience Wraps any Effect with retry-on-failure. Configure attempts, delay, backoff. On exhaustion: a structured Failure. vi. Parallel(effects, next) concurrency Runs Effect trees concurrently via Promise.all. Ask context flows into every branch. First Failure short-circuits.

plus the composers: effectPipe(...fns): sequential runEffect(effect, ctx?): the interpreter configureEffect(opts): global hooks

Complete example: user registration flow import { Success, Failure, Command, effectPipe, runEffect } from 'pure-effect';

// Pure functions, no I/O, no imports, instantly testable. function validateRegistration(input) { if (!

input.email?.includes('@')) return Failure('Invalid email.'); if (input.password?.length < 8) return Failure('Password too short.'); return Success(input); }

function ensureEmailAvailable(found) { return found ? Failure('Email already in use.') : Success(true); }

// Commands: side effects described as data, executed by the interpreter. function findUserByEmail(email) { const cmdFindUser = () => db.findUserByEmail(email); return Command(cmdFindUser, (found) => Success(found)); }

function saveUser(input) { const cmdSaveUser = () => db.saveUser(input); return Command(cmdSaveUser, (saved) => Success(saved)); }

// Pipeline: compose into a single flow. const registerUserFlow = (input) => effectPipe( validateRegistration, () => findUserByEmail(input.email), ensureEmailAvailable, () => saveUser(input) )(input);

// Test: assert on structure, no mocks, no I/O. const flow = registerUserFlow({ email: '[email protected]', password: 'password123' }); assert.equal(flow.cmd.name, 'cmdFindUser'); assert.equal(flow.next(null).cmd.name, 'cmdSaveUser');

// Run: hand the tree to the interpreter at the boundary. const saved = await runEffect(registerUserFlow(input), { flowName: 'register' });

03  /  features What you get out of the box.

Test without a database Assert what your code would do. Pipelines return inert objects. Walk the tree and check each step: no mock, no in-memory fake, no container. assert.equal(step.cmd.name, 'cmdFindUser'); assert.equal(step.type, 'Command');

Replay production runs Step through the failed request locally. Record what each command returned in production, then feed those results back into the same tree to retrace the exact path, no infrastructure attached.

let step = flow; while (step.type === 'Command') step = step.next(recorded[step.cmd.name]); // replays the recorded path, no I/O

Retry as data Test resilience without waiting. Wrap any Effect with retry semantics. Because the config is plain data, tests assert on it directly: no timers, no sleeps, no flaky timing. Retry(effect, { attempts: 3, delay: 200, backoff: 2 }); // 200·400·800

Parallel execution Concurrent branches, ordered results. Promise.all semantics with first-failure short-circuiting. Ask context flows into every branch automatically. Parallel( [getUser(id), getPerms(id)], ([user, perms]) => Success({ user, perms }) );

Dependency injection Context without polluting signatures. Resolve tenant, trace id, or config from the framework layer. Domain functions stay clean and just Ask. Ask((ctx) => { // ctx.tenant comes from runEffect return Command(...) });

OpenTelemetry Lifecycle hooks for tracing. onRun, onStep, and onBeforeCommand let you wrap workflows in spans without touching domain code. configureEffect({ onRun: (eff, pipeline, name) => tracer.startActiveSpan(name, pipeline) });

04  /  positioning Where it fits, and where it doesn't. Pure Effect describes one finite operation as a tree you read before it runs. That is its strength and its boundary: it is for request-shaped operations, not background processes.

Library DESCRIPTION Choose pure-effect if… Effect-TS A full functional ecosystem with fibers, streaming, schema validation, and structured concurrency. Powerful, with a steep learning curve. …you need testable pipelines, retry, and dependency injection that can be learned in an afternoon. fp-ts Brings category-theory abstractions such as functors, monads, and applicatives to TypeScript. Teaches a lot of vocabulary along with effects. …you want effects-as-data without the academic vocabulary. async/await + mocks The default. Simple for small surfaces. Falls apart when test isolation matters: mocks drift from real drivers and tests turn into theater. …test isolation is a real pain point in your codebase.

05  /  ai AI writes the flow. You read it before it runs. AI code generators emit async/await: to see what it does you run it, and every check touches infrastructure. Because a Pure Effect flow is plain data, you can read its control flow before anything executes: which commands it issues, in what order, down which branch.

async / await: run to verify // Did the AI handle the error path? // Did it thread context correctly? // You won't know until you run it. async function registerUser(input) { const found = await db.findUser(input.email); if (found) throw new Error('Email in use'); return db.saveUser(input); // awaited? who knows. } You audit by executing. Every verification touches infrastructure.

pure-effect: read to verify // AI generated this flow.