# magnitudedev/browser-agent

Open-source, vision-first browser agent

Repository: https://github.com/magnitudedev/browser-agent
Canonical: https://ross.abutalabs.com/products/browser-agent
Homepage: https://magnitude.run
Language: TypeScript
License: Apache-2.0
License Family: permissive
Topics: test, typescript, automation, browser, framework, javascipt, ai, playwright, selenium, rpa
Last push: 2026-02-08T20:51:06+00:00

## Health v2 (maintenance only)
Score: 61/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 66, release rhythm 69, longevity 37
- inputs: {"age_days": 531, "days_push": 206, "days_rel": 206, "gap_med": 0, "n_releases_24m": 40}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4121, forks 236 (observed 2026-08-28T04:08:35.980041+00:00)

## What it is
Magnitude is an open-source, vision-first AI browser agent written in TypeScript that controls the browser through natural language commands. It combines high-level task execution, low-level mouse/keyboard actions, structured data extraction with Zod schemas, and a built-in test runner with visual assertions for web apps.

## Use cases
- automate repetitive browser tasks using natural language
- run AI-driven end-to-end tests on my web app
- extract structured data from web pages into a schema
- integrate between web apps that have no API
- replace flaky selenium selectors with vision-based assertions
- build my own browser agent on top of an existing framework
- scrape website data by describing what I want instead of writing selectors

## When to choose
- You need automation that works across arbitrary or changing UIs without brittle CSS selectors
- You want AI-assisted E2E testing with built-in visual verification and assertions
- You need to extract structured data (e.g. Zod-validated) from web interfaces
- You want a programmatic act/extract building block for custom agent workflows
- You must integrate web apps that lack APIs

## When to avoid
- You need deterministic, fast, low-cost automation without LLM inference overhead - plain Playwright or Selenium is cheaper and more predictable
- You operate offline or in air-gapped environments since it depends on vision AI models
- You do very high-volume scraping where per-action LLM latency and cost are prohibitive
- You need to automate native desktop or mobile applications rather than browsers

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, e2e-testing, testing, web-scraping, workflow-automation
- domain: testing, web-development, developer-tools
- platform: browser, cli, cross-platform
- tags: browser-automation, vision-ai, natural-language-control, playwright, rpa, selenium-alternative, visual-assertions, data-extraction, zod-schemas, test-runner, llm-powered, ai-agents, automation, nodejs

## Member repositories
- magnitudedev/browser-agent (main) score 61

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:35.980041+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:06.557002+00:00, confidence not recorded.
  - readme: https://github.com/magnitudedev/browser-agent (fetched 2026-08-28T04:08:35.980041+00:00, sha 2ebd9a80ac23)
  - homepage: https://magnitude.run (fetched 2026-08-29T09:14:35.428076+00:00, sha 24f38d05c522)
- Data as of 2026-08-30T08:39:29.467469+00:00.