What problem it solves

Teams need to build workflows, applications, and AI agents while retaining code review, deployment controls, and isolated execution. The platform also needs to let scripts become user interfaces, APIs, scheduled jobs, and composable flows without requiring teams to manage every interface separately.

Key features

• Code-first development — Write scripts in multiple programming languages and use typed arguments to generate forms, HTTP endpoints, and CLI commands. Scripts can be composed into flows and used to build more complete interfaces.

  Source: README, typescript-client

• Composable workflows — Chain scripts into flows or define workflows as code, with visual editing and YAML also available. Each task can run as an individual job with its own logs.

  Source: README

• AI-assisted review workflow — AI sessions can generate scripts, flows, and apps in a workspace fork with a preview and a diff. Protection rules and deployment workflows let engineers review changes before they reach production.

  Source: README

• Local and browser-based tooling — Developers can sync a workspace with local files and Git, edit projects in VS Code, and run scripts and flows from the command line. Browser-based AI sessions provide planning, skills, MCP servers, live previews, and reviewable diffs.

  Source: README

• Isolated execution and data access — Jobs run in isolated workers with filesystem, resource, and process-memory protections. Workspace data tables, DuckDB, and DuckLake support SQL-based access to application and file-based data.

  Source: README, sandbox-image

Quick start

Copied verbatim from the project's README.

curl https://raw.githubusercontent.com/windmill-labs/windmill/main/docker-compose.yml -o docker-compose.yml
curl https://raw.githubusercontent.com/windmill-labs/windmill/main/Caddyfile -o Caddyfile
curl https://raw.githubusercontent.com/windmill-labs/windmill/main/.env -o .env

docker compose up -d

import * as wmill from "windmill-client";
import dayjs from "dayjs"; // any npm package, resolved and pinned automatically

export async function main(
  customerId: string,
  plan: "starter" | "pro", // a union of strings becomes a dropdown
  trialDays = 14 // defaults infer the type
) {
  const trialEnd = dayjs().add(trialDays, "day").toISOString();

  // a data table: the workspace's Postgres, queried without a connection string
  const sql = wmill.datatable();
  await sql`INSERT INTO trials (customer_id, plan, trial_end) VALUES (${customerId}, ${plan}, ${trialEnd})`.fetch();

  // secrets are permissioned by path
  const slackToken = await wmill.getVariable("f/sales/slack_token");
  await fetch("https://slack.com/api/chat.postMessage", {
    method: "POST",
    headers: { Authorization: `Bearer ${slackToken}`, "Content-Type": "application/json" },
    body: JSON.stringify({ channel: "#sales", text: `New ${plan} trial: ${customerId}` }),
  });

  console.log(`Trial started for ${customerId}`); // logs stream live in the UI
  return { customerId, plan, trialEnd }; // returned as JSON
}

Who it may suit

Inference: may suit engineering and business teams that want to build code-based workflows and applications on a shared, self-hostable platform. It may also suit teams using AI-assisted development that require explicit review and deployment controls.

Topics to discuss

• Which review and promotion controls should be standard for AI-generated scripts, flows, and apps?

• How should workflow tasks exchange data between scripts, flows, and data tables?

• Which self-hosting setup and worker configuration patterns should receive more documentation and examples?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/windmill-labs/windmill#readme

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