# MaaXYZ/MaaFramework

基于图像识别的自动化黑盒测试框架 | An automation black-box testing framework based on image recognition

Repository: https://github.com/MaaXYZ/MaaFramework
Canonical: https://ross.abutalabs.com/products/maaframework
Homepage: https://maafw.com
Language: C++
License: LGPL-3.0
License Family: copyleft
Topics: computer-vision, black-box-testing
Last push: 2026-08-24T11:25:02+00:00

## Health v2 (maintenance only)
Score: 91/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 83, longevity 87
- inputs: {"age_days": 1227, "days_push": 9, "days_rel": 32, "gap_med": 2.0, "n_releases_24m": 97}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4722, forks 538 (observed 2026-08-28T04:08:57.523115+00:00)

## What it is
MaaFramework is an automation black-box testing framework based on image recognition, rewritten from the experience of the MAA (MaaAssistantArknights) project. It offers a low-code pipeline protocol with high extensibility, letting developers build image-recognition-driven UI automation and testing programs across Windows, Linux, macOS, and Android.

## Use cases
- automate android game tasks with image recognition
- write black-box UI tests without access to app source code
- build low-code automation pipelines driven by screenshots
- create custom automation assistants for mobile apps
- run cross-platform gui automation scripts
- integrate image-based testing into python or node projects

## When to choose
- you need to automate or test apps via the gui without source access
- you want low-code pipeline definitions with extensible custom modules
- you target android or desktop apps across windows, linux, and macos
- you want bindings for python, c#, go, rust, or node

## When to avoid
- you need unit or api-level testing rather than visual ui automation
- your app exposes a proper test automation api like appium or playwright targets
- you require pixel-perfect ocr-heavy document processing rather than ui element recognition

## Facets
- artifact type: framework
- maturity: active
- function: testing, e2e-testing, computer-vision, workflow-automation
- domain: testing, computer-vision, developer-tools
- platform: windows, cpp, python, cross-platform
- tags: image-recognition, black-box-testing, low-code, game-automation, pipeline-protocol, automation, linux, macos, android

## Member repositories
- MaaXYZ/MaaFramework (main) score 91

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:57.523115+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 2026-08-29T18:19:05.261486+00:00, confidence not recorded.
  - readme: https://github.com/MaaXYZ/MaaFramework (fetched 2026-08-28T04:08:57.523115+00:00, sha 3153d15d8dd3)
  - homepage: https://maafw.com (fetched 2026-08-29T09:02:48.840557+00:00, sha 8d65bdfb732e)
- Data as of 2026-08-30T08:39:29.467469+00:00.