Reproduce a production bug on your laptop, no database required.

Pure Effect is a zero-dependency effect library for JavaScript and TypeScript with time-travel debugging. 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
            

registerUserFlow(input) ↓ effect tree

Success validateRegistration sync · pure

Command findUser deferred I/O

Success ensureEmailAvailable sync · pure

Command saveUser deferred I/O

3.4 KB

min + gzipped

dependencies

primitives total

Terminal session: timeTravel replays a recorded checkout flow, narrating three recorded steps and ending in a Failure, with no database and no network — A recorded production failure, replayed locally with timeTravel(checkoutFlow, trace).

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.

Success(value)

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?, meta?)

deferred I/O

A side effect described as data. cmd is the function that would run; next turns its result into the next Effect, defaulting to a pass-through. meta.name names the step for traces, replay, and spans.

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.

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 machinery:
          effectPipe(...fns):
            sequential
          runEffect(effect, ctx?):
            the interpreter
          configureEffect(...configs):
            global hooks, merged
          recordEffect / replayEffect / timeTravel:
            record & replay
        

            Complete example: user registration flow
          

import { Success, Failure, Command, effectPipe, runEffect } from 'pure-effect';

// Pure. No I/O, instantly testable.
const validateRegistration = (input) => {
  if (!input.email.includes('@')) return Failure('Invalid email.');
  if (input.password.length < 8) return Failure('Password too short.');
  return Success(input);
};

// A Command object. It does NOT call the database.
const ensureEmailAvailable = (input) => {
  const cmdFindUser = () => db.findUser(input.email);
  return Command(cmdFindUser, (found) => (found ? Failure('Email already in use.') : Success(input)));
};

// With no continuation, the result passes straight through.
const saveUser = (input) => {
  const cmdSaveUser = () => db.saveUser(input);
  return Command(cmdSaveUser);
};

// Pipeline: every step receives the value the previous step produced.
const registerUserFlow = (input) =>
  effectPipe(validateRegistration, ensureEmailAvailable, saveUser)(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 result = 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');

Time-travel debugging

Step through the failed request locally.

Record what each Command returned in production, then replay the trace through the same flow to retrace the exact path it took: narrated, timed, and with zero I/O. A divergent flow raises a TimeParadox.

const { trace } = await recordEffect(checkoutFlow, input);
await timeTravel(checkoutFlow, trace); // no database, no network

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. Configurations merge, so tracing and production recording share the hooks.

configureEffect(telemetryHooks(), recordingHooks({ sink })); // one call, both concerns

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. Inspect it before it runs.
const flow = registerUserFlow(input);

assert.equal(flow.type, 'Command');
assert.equal(flow.cmd.name, 'cmdFindUser');

const step2 = flow.next(null);
assert.equal(step2.cmd.name, 'cmdSaveUser');
// Control flow confirmed. Nothing executed.

You audit by reading the tree. No database, no network.

This confirms the shape of generated code, not its correctness, but it rules out a wrong code path before anything runs.

06 / install

Six primitives.
Zero dependencies.
See what your code does before it runs.

Star on GitHub Read the docs

              $
              npm install pure-effect
            