2025-06-26 14:30:00
github.com
Magnitude uses vision AI to enable you to control your browser with natural language
- 🧭 Navigate – Sees and understands any interface to plan out actions
- 🖱️ Interact – Executes precise actions using mouse and keyboard
- 🔍 Extract – Intelligently extracts useful structured data
- ✅ Verify – Built-in test runner with powerful visual assertions
You can use it to automate tasks on the web, integrate between apps without APIs, extract data, test your web apps, or as a building block for your own browser agents.
// Magnitude can handle high-level tasks
await agent.act('Create a task', {
// Optionally pass data that the agent will use where appropriate
data: {
title: 'Use Magnitude',
description: 'Run "npx create-magnitude-app" and follow the instructions',
},
});
// It can also handle low-level actions
await agent.act('Drag "Use Magnitude" to the top of the in progress column');
// Intelligently extract data based on the DOM content matching a provided zod schema
const tasks = await agent.extract(
'List in progress tasks',
z.array(z.object({
title: z.string(),
description: z.string(),
// Agent can extract existing data or new insights
difficulty: z.number().describe('Rate the difficulty between 1-5')
})),
);
This will create a new project and walk you through the steps for setting up Magnitude. It will also create an example script that you can run right away!
To install the test runner for use in an existing web app, please run:
npm i --save-dev magnitude-test && npx magnitude init
This will create a basic tests directory tests/magnitude
with:
magnitude.config.ts
: Magnitude test configuration fileexample.mag.ts
: An example test file
For information on how to run tests and integrate into CI/CD see here.
Note
Magnitude requires a large visually grounded model. We recommend Claude Sonnet 4 for the best performance, but are also compatible with Qwen-2.5VL 72B. See docs for more information.
❌ Problem #1: Most browser agents draw numbered boxes around page elements – doesn’t generalize well due to complex modern sites
✅ Solution: Vision-first architecture
- Visually grounded LLM specifies pixel coordinates
- True generalization independent of DOM structure
- Future-proof architecture for desktop apps, VMs, etc.
❌ Problem #2: Most browser agents follow “high-level prompt + tools = work until done” – works for demos, not production
✅ Solution: Controllable & repeatable automation
- Flexible abstraction levels (granular actions vs. flows)
- Custom actions + prompts at agent and action level
- Deterministic runs via native caching system (in progress)
Please see our docs for more information on how to best build Magnitude automations and test cases.
If you are an enterprise and want more features or support, feel free to reach out to us at founders@magnitude.run or schedule a call here to discuss your needs.
You can also join our Discord community for help or any suggestions!
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