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Basic Information

Magnitude is an open source, vision-first browser agent platform designed to let developers control browsers with natural language and visual understanding. It provides a library and tooling to build browser automations and agents that see interfaces, plan actions, execute mouse and keyboard interactions, extract structured data from pages, and run visual test assertions. The project includes a quickstart scaffold via npx create-magnitude-app, example scripts, and a test runner package for integrating visual tests into existing web apps and CI. Magnitude emphasizes a visually grounded LLM approach that specifies pixel coordinates rather than relying solely on DOM selectors, and it is positioned as a building block for automations, integrations between apps without APIs, data extraction pipelines, and automated visual testing workflows.

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App Details

Features
Magnitude centers on a vision-first architecture that uses visually grounded large models to specify pixel coordinates for robust interaction across complex sites. Core capabilities highlighted are Navigate to understand interfaces and plan steps, Interact to perform precise mouse and keyboard actions, Extract to return structured data using provided schemas such as zod, and Verify via a test runner with visual assertions. The repo includes scaffolding through create-magnitude-app, a magnitude-test package to initialize tests, example test files and configuration, support notes for recommended visually grounded models like Claude Sonnet 4 and compatibility with Qwen-2.5VL 72B, and design choices for flexible abstraction levels, custom actions and prompts, and a native caching mechanism for deterministic runs in progress.
Use Cases
Magnitude helps developers and QA teams automate complex web tasks that are brittle when using DOM-only approaches by relying on visual grounding so agents generalize across modern interfaces. It enables end-to-end browser automation from high-level instructions to low-level actions, simplifies extracting structured data from pages using schemas, and provides a built-in test runner with visual assertions to validate UI behavior and integrate into CI. The platform is useful for integrating apps without official APIs, building specialized browser agents, and creating repeatable, controllable automations with custom actions and prompts. Documentation and examples accelerate onboarding and recommended model guidance supports choosing appropriate visually grounded LLMs for best results.

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