Build

Build mission-critical internal tools and data pipelines that integrate directly with your existing stack and resources using code with a powerful WebIDE or locally using our CLI and your favorite editor and AI agent.

Write scripts in 20+ languages (Python, TS, Go...) with full LSP support, auto-generated UI, managed dependencies and turn them into instant endpoints or hooks for pubsub events.

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Use Windmill locally via our CLI and VS Code extension. Leverage AI-assisted rules for Cursor and Claude, and deploy through automated Git-sync pipelines across staging and production.

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Build complex flows without complexity

Build complex Flows from atomic scripts, either from your workspace or the Hub.

Approval

Suspend the execution of a flow and request confirmation from given users before resuming.

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Data pipelines

Orchestrate pipelines & ETLs with observability and control at any step.

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Advanced Features

Build powerful flows with advanced features like triggers, error handling, retries, loops and branches.

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Workflows as code

Build, version, and manage your workflows as code with the Windmill SDK. Use your favorite IDE, version control system, and CI/CD pipeline to manage your workflows.

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1from wmill import task
2
3import pandas as pd
4import numpy as np
5
6@task()
7# You can specify tag to run the task on a specific type of worker, e.g. @task(tag="custom_tag")
8def heavy_compute(n: int):
9    df = pd.DataFrame(np.random.randn(100, 4), columns=list('ABCD'))
10    return df.sum().sum()
11
12
13@task
14def send_result(res: int, email: str):
15    # logs of the subtask are available in the main task logs
16    print(f"Sending result {res} to {email}")
17    return "OK"
18
19def main(n: int):
20    l = []
21
22    # to run things in parallel, simply use multiprocessing Pool map instead: https://docs.python.org/3/library/multiprocessing.html
23    for i in range(n):
24        l.append(heavy_compute(i))
25    print(l)
26    return send_result(sum(l), "[email protected]")
27