Deploy OpenClaw for AI‑Powered UI Automation Testing on Cloud

What is OpenClaw?

OpenClaw is an AI‑assistant runtime environment that acts as a smart gateway, integrating large language models such as Claude, GPT, Gemini, and Kimi with chat platforms like Feishu, DingTalk, WeChat, and Telegram. It enables AI to operate directly within chat windows, turning natural‑language prompts into UI actions, logic checks, and result analysis.

Core Capabilities

Autonomous Execution : Operates browsers, software, and file systems like a human.

7×24 h Availability : Runs without human supervision.

Local Execution : Deploys on a personal computer or server, bypassing cloud sandbox restrictions.

Intelligent Decision‑Making : Executes predefined scripts and makes context‑aware judgments.

Installation Options

OpenClaw can be installed either locally or on a cloud server. Local installation is faster and keeps data private, but it requires a secondary machine because the agent runs with high privileges. Cloud installation (e.g., on Alibaba Cloud or Tencent Cloud) offers lightweight, flexible configuration at the cost of slightly lower speed and additional server fees.

For first‑time users we recommend the cloud mode to reduce trial‑and‑error costs.

Preparing the Cloud Environment

Provision a Cloud Server (Tencent Cloud example) Open the Tencent Cloud console, click “Deploy My AI Assistant”, choose a lightweight 2‑CPU 2 GB instance (≈20 CNY/month), and select the latest OpenClaw image.

Connect an AI Large Model Select a model provider (e.g., Qianwen), enter the corresponding API key, and save.

Configure the IM Channel (Feishu) Enable the Feishu channel, then create a custom Feishu app to obtain App ID and App Secret . Fill these values into the OpenClaw channel configuration and apply.

Set Event & Permission Scopes In the Feishu developer console, enable “Receive Message” events and add the following permission scopes (replace the placeholder JSON with the exact content):

{
  "scopes": {
    "tenant": [
      "im:message",
      "im:message.p2p_msg:readonly",
      "im:message.group_at_msg:readonly",
      "im:message:send_as_bot",
      "im:resource",
      "contact:user.base:readonly",
      "im:message.group_msg",
      "im:message:readonly",
      "im:message:update",
      "im:message:recall",
      "im:message.reactions:read",
      "docx:document:readonly",
      "drive:drive:readonly",
      "wiki:wiki:readonly",
      "bitable:app:readonly",
      "task:task:read",
      "contact:contact.base:readonly",
      "docx:document",
      "docx:document.block:convert",
      "drive:drive",
      "wiki:wiki",
      "bitable:app",
      "task:task:write"
    ],
    "user": []
  }
}

Confirm the scopes, then create a version in Feishu, add the API key, and finish the setup.

Validate the Connection

Log into the cloud server, open the OpenClaw command line, and paste the test command copied from the Feishu chat. If the command returns a response page, the connection is successful. Sending a message in Feishu should now trigger a reply from the OpenClaw assistant, confirming end‑to‑end integration.

Running UI Automation Tests

To perform a UI test, simply type a natural‑language command in the Feishu chat, such as “Test the login page”. OpenClaw will autonomously launch a browser, navigate to the login page, interact with UI elements, verify logic, and return the test result—all without writing any script.

Advantages of this AI‑driven approach include:

Lower Technical Barrier : No programming required; tests are created via natural language.

Higher Coverage : Easily test edge cases and multiple scenarios.

Reduced Maintenance : UI changes do not require script rewrites.

Unattended Execution : 7×24 h automated testing.

Seamless Feishu Integration : Issue commands and receive results directly in chat.

Team Collaboration : Share test tasks in group chats for discussion.

Conclusion

OpenClaw combined with Feishu exemplifies the future of UI automation testing: AI‑driven, self‑adapting, and collaborative. It shifts testing from script‑centric to intelligent, reduces maintenance costs, and creates a closed loop of execution, feedback, and teamwork, empowering testers to focus on design and analysis rather than repetitive execution.