windmill

Open-source, self-hostable code-first platform for workflows, apps and AI agents, running on the fastest self-hostable orchestrator. Engineers write code in 20+ languages and sync it with Git. Business teams build on the same platform with AI, while review, promotion between environments and isolated workers keep engineers in control of what ships. Self-hostable alternative to Lovable, Retool, n8n and a simplified Temporal.

Scripts are turned into sharable UIs, APIs and cron jobs automatically, and can be composed together into flows, data pipelines, AI agents or full-code React and Svelte apps. Supported languages: Python, TypeScript, Go, Bash, SQL, GraphQL, PowerShell, Rust, and more.

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Windmill - Code-first platform for workflows, apps and AI agents

Windmill is fully open-sourced (AGPLv3) and Windmill Labs offers dedicated instances and commercial support and licenses.

https://github.com/user-attachments/assets/d80de1d9-64de-4d89-aacd-6df23fa81fc4

• Windmill - Code-first platform for workflows, apps and AI agents
  • Build with AI, ship with review
  • Main Concepts
  • What the code looks like
  • Data: data tables, DuckDB and DuckLake
  • Develop locally or in the browser
  • Stack
  • Fastest Self-Hostable Workflow Engine
  • Security
  • Performance
  • Architecture
  • How to self-host
    • Docker compose
    • Kubernetes (Helm charts)
    • Cloud providers
    • OAuth, SSO & SMTP
    • License
    • Integrations
  • Environment Variables
  • Contributing
  • Copyright

Build with AI, ship with review

Business teams and engineers build on the same Windmill instance. What the AI builds is an ordinary script, flow or app, and goes through the same review as code written by engineers:

• AI sessions build scripts, flows and apps with AI in a workspace fork, with a live preview and a diff to review before deploying.
• Workspace roles decide what each team can author, and folders scope the resources, skills and MCP servers its AI can reach.
• Protection rulesets block direct deployment, so changes reach production only after an engineer reviews and deploys them.
• Dev workspaces keep their own credentials, and promote to prod through Compare & Deploy or a Git pull request.
• Worker groups run AI-built code on its own workers, or on a separate instance.

See let business teams build with AI safely for the full setup. Engineers keep writing code locally with the CLI, the VS Code extension and their coding agent, synced with Git.

Main Concepts

1. Define a minimal and generic script in Python, TypeScript, Go or Bash that solves a specific task. The code can be defined in the provided Web IDE or synchronized with your own GitHub repo (e.g. through VS Code extension): provided Web IDE or synchronized with your own GitHub repo (e.g. through VS Code extension):

2. Your scripts parameters are automatically parsed and generate a frontend.

3. Make it flow! You can chain your scripts or scripts made by the community shared on WindmillHub.

4. Build complex UIs on top of your scripts and flows.

Scripts and flows can be triggered by schedules, webhooks, HTTP routes, Kafka, WebSockets, emails, and more.

Build your entire infra on top of Windmill!

What the code looks like

A script is a plain function. Its typed arguments become a generated form, an HTTP endpoint and a CLI command:

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
}

Workflows are code too. Each task runs as its own job with its own logs, and waiting for an approval releases the worker:

import { task, waitForApproval, workflow } from "windmill-client";

async function plan(env: string) {
  return { env, changes: 3 };
}

async function apply(plan: { env: string; changes: number }) {
  return `applied ${plan.changes} changes to ${plan.env}`;
}

export const main = workflow(async (env: string) => {
  const changes = await task(plan)(env);
  const approval = await waitForApproval({ timeout: 3600 });
  if (!approval.approved) {
    return `rejected by ${approval.approver}`;
  }
  return await task(apply)(changes);
});

Flows can also be built in the visual flow editor or as YAML, with scripts like the first one as steps. See workflows as code.

Data: data tables, DuckDB and DuckLake

• Data tables give each workspace managed Postgres tables, queried with type-checked SQL from TypeScript, Python and DuckDB scripts without exposing a connection string.
• DuckDB scripts query S3 and Azure Blob files (CSV, Parquet, JSON), data tables, Postgres, MySQL and BigQuery in SQL, on your workers.
• DuckLake stores large datasets as lakehouse tables in your workspace object storage, queried in plain SQL.
• Pipelines (alpha) wire scripts by the data they read and write instead of a hand-drawn DAG. This script runs whenever events is materialized, and materializes its own table:

-- pipeline
-- on ducklake://main/events
-- materialize ducklake://main/events_by_kind
ATTACH 'ducklake://main' AS dl;
SELECT kind, count(*) AS n
FROM dl.events
GROUP BY kind;

Existing dbt projects run unmodified as scripts. See the pipelines quickstart.

Develop locally or in the browser

Engineers work from their editor and coding agent, business teams build with AI in the browser, and both land in the same workspace:

https://github.com/user-attachments/assets/c541c326-e9ae-4602-a09a-1989aaded1e9

You can run scripts locally by passing the right environment variables for the wmill client library to fetch resources and variables from your instance. See local development docs.

Stack