Turn robots that breakinto workflowsyou fix by talking.

AI handles the web operations, people make the decisions. Business automation you build and fix by talking to an AI assistant.

Platforms
  • macOSBeta
  • WindowsComing
  • LinuxComing
A Kitewell run: a workflow that collected orders from a supplier portal is waiting for the owner's approval
Actual screen: a workflow waiting for approval

The interface comes in six languages, including Japanese and English. Chat and steps work in any language your chosen AI model supports. Screens are from a demo environment with fictional data.

When your automation stops, can you fix it?

  • It stops when a screen changes

    A small change to a web system stops the robot.

  • Only the builder can fix it

    When that person moves on, nobody can.

  • Nobody keeps track

    Robots multiply across departments, unseen.

Build and fix in the language of the work.

Ask, and the workflow is built

Say what you want in your own words, and the assistant builds a workflow and proposes it.

Nothing is saved until you choose Apply.

Feature details

A workflow the assistant proposed, with Edit in editor, Reject, and Apply buttons
Actual screen: nothing the assistant proposes is saved until you choose Apply

Web operations are sentences

Write steps such as “enter the login ID”, and AI reads the screen to carry them out. A small screen change does not mean rewriting the steps.

Passwords are named, never shown to the AI.

Feature details

The workflow editor, with web steps written in plain language such as entering the login ID
Actual screen: web steps written in plain language

When it stops, fix it on the spot

From the step that stopped, ask the assistant about the cause. Once fixed, run again from that step.

Completed steps keep their results.

Feature details

A Kitewell run: recording to the sales system failed, with an Ask the assistant button
Actual screen: the step that stopped, and a button to ask the assistant about it

Your business data stays in your environment

  • Workflows, run logs, and credentials stay on your machine
  • You choose the AI model, including in-house and local models
  • Credentials are encrypted, and AI never sees their values

About security

Open-source Dagu underneath

Kitewell runs on Dagu, the open-source workflow engine Descarty develops. You are never locked in to one vendor.

GitHub stars
4,100+
License
GPL-3.0

About open source

Everything business automation needs

  • Schedules and catch-up of missed runs
  • Branches and loops
  • API import (OpenAPI)
  • Server operations over SSH
  • File transfer
  • Docker containers
  • AI agents such as Claude Code
  • Alerts to Slack, Teams, and email
  • Queues to limit concurrent runs
  • Backup and restore
  • MCP and REST API
  • Interface in six languages

Start by testing it on your own work

Choose one to three real tasks, and a PoC measures the effect on the same screens and data as production.

  1. 1

    Interview

    We learn about your work, problems, and RPA.

  2. 2

    Selection

    Agree on the tasks and success criteria.

  3. 3

    Build

    Build workflows on your real screens and data.

  4. 4

    Verify

    Run them on real data and report the results.

From automation that breaks to automation you can fix

Tell us which work you have in mind, and we will propose how a PoC would run.