Quick Summary: 📝
Shellwright is a tool that brings Playwright's capabilities to the command line, enabling AI-driven terminal automation, recording of terminal sessions as videos, and taking screenshots. It acts as an MCP (Model Context Protocol) server, allowing LLMs and agents to interact with and control terminal applications.
Key Takeaways: 💡
✅ Shellwright enables AI agents to automate complex, interactive terminal applications (like Vim or K9s) using natural language prompts.
✅ It acts as Playwright for the command line, exposing the shell environment via the Model Context Protocol (MCP) for LLM consumption.
✅ The tool automatically generates visual artifacts, including step-by-step screenshots and GIF/video recordings of the entire automation process.
✅ Developers can use shellwright to create high-quality, reproducible documentation and visual tutorials instantly.
✅ It solves the long-standing problem of reliably automating text-based user interfaces (TUIs) and interactive CLI tools.
Project Statistics: 📊
- ⭐ Stars: 23
- 🍴 Forks: 3
- ❗ Open Issues: 15
Tech Stack: 💻
- ✅ TypeScript
Shellwright is essentially the Playwright equivalent for your command line interface. If you've ever struggled to automate complex terminal interactions—especially those involving interactive tools like Vim or K9s—this project is your new best friend. It bridges the gap between powerful Language Models (LLMs) and the traditional shell environment, allowing AI agents to execute multi-step commands, navigate TUI applications, and even handle tricky scenarios like closing Vim gracefully, which is notoriously difficult for scripted automation.
The core of shellwright relies on the Model Context Protocol (MCP). Think of MCP as a standardized API that allows AI agents to discover and use external tools. Shellwright runs as an MCP server, exposing the terminal environment as a controllable tool. When your LLM agent receives a prompt—like "Open htop and show me my resources"—it translates that natural language request into specific, automated terminal actions via the shellwright tool. This means the AI isn't just running static commands; it's interacting dynamically, reading the output, and responding to the state of the terminal just like a human user would, making it perfect for handling text-based user interfaces.
For developers, the advantages are massive. Firstly, automation becomes visual and verifiable. Shellwright doesn't just execute commands; it captures the entire interaction. It can generate step-by-step screenshots and full GIF or video recordings of the terminal session. This is revolutionary for creating documentation, tutorials, or rich recordings for books and blogs. Imagine instantly generating a perfect, reproducible GIF demonstrating a complex Kubernetes operation using K9s, all from a simple prompt. This capability saves immense time compared to manual screen recording and editing.
Getting started is surprisingly straightforward. You configure your LLM (using tools like Claude Code or Cursor) to recognize the shellwright MCP server. Once configured, you simply prompt the agent to perform the task, specifying that you want visual artifacts saved to a directory. This tool transforms tedious manual processes—like creating demo videos or setting up complex environment tests—into simple, natural language requests. It elevates the capabilities of your AI agents, turning them into reliable terminal operators who can finally handle interactive applications without getting stuck, thereby enhancing developer productivity and documentation quality significantly.
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