30 分鐘從零打造 OpenClaw 個人助理

In the final episode of Zero to Agent in 30 Minutes, AI engineer Sajal Sharma showed how to build a personal assistant on OpenClaw that tracks your tasks and notes and handles scheduled work on its own.

A useful personal assistant has to be reachable when you’re away from your computer and keep running when it has scheduled work to do. OpenClaw supports both, and you decide where the agent runs, which models it uses, and how it accesses your data.

How to go from a fresh install to scheduled automations, step-by-step

Install your OpenClaw instance. Sajal recommends starting locally while you learn how it works, then moving to an always-on machine or hosted environment if you need continuous availability.
Connect the channels you use. Add a messaging channel such as Telegram, WhatsApp, or Slack so you can reach the assistant away from your desk. Sajal used Telegram in his demo to send requests and receive notifications from the same assistant.
Set up the assistant’s workspace. Give the agent a place to keep tasks, notes, skills, and memory, along with guidance files that define how it behaves and how it should work with the files in that workspace.
Use it in your daily workflows. Sajal asked the assistant for an overview of current projects and checked progress toward a reading goal. He also had it search the web for a book series and add the books it found to his reading list.
Add proactive automations. Schedule work the assistant should handle on its own. Sajal added a Telegram reminder and a morning briefing designed to pull from tasks, a calendar, and recent email. Recurring checks and webhooks can also trigger the agent when something changes in another system.

Thanks for following Zero to Agent

This episode closes out Zero to Agent in 30 Minutes. Thanks for watching and building along with our presenters throughout the series.

If you’d like to keep following how agents can take on more complex work, tune in to The Monday Morning Agent, which debuts Monday, October 12, at 8am PT/11am ET. The eight-episode series follows the development of an increasingly autonomous data agent working with real play-by-play data. Learn more and save your seat or follow along here on Radar.