Gemini 2.5 is not just a chatbot—it's a tool-using engine. The Gemini 2.5 Computer Use model wires tool-calling and browser automation into the LLM stack, and the debate is about speed, reliability, and governance [1].
Tool-calling and Playwright scripting - The stack leans on tool-calling and structured outputs. The Gemini CLI pairs with the Chrome devtools MCP for browser automation; some say Jest and Playwright are faster than the MCP approach [1]. A sample repo shows using the default computer_use tool [1].
Herd and Trail automations — The thread describes agents that build, test and heal trails, which are packaged browser automations that don’t require browser-use LLMs to run and are cheaper and more reliable. Trails are then abstracted as a REST API and MCP [1]. The automation stack is home-grown to enable distributed orchestration and doesn’t rely on Puppeteer nor Playwright; the browser automation API is similar to ease adoption [1][6]. The project also ships a CLI and npm & Python packages [4][5].
Speed vs reliability — Critics say performing an action, reading results, then waiting for the next tool call is slow; others lean toward writing Playwright scripts or manual navigation first to seed automation [1].
Bottom line: Gemini 2.5’s tool-use signals a fork in LLM governance—speed, reliability and control will determine which automation path wins [1].
References
Gemini 2.5 Computer Use model
Gemini 2.5 computer-use model; tool-calling, browser automation, Playwright scripting; debates speed, reliability, CAPTCHA handling, governance, and comparisons with MCP options.
View source