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Casely vs OpenTest.AI

Casely

casely.digital

OpenTest.AI

opentest.ai

Side by side

FigureCaselyOpenTest.AI
Price from—Free
Free plan—Yes
Free trial——
Open source——
WebYes—
WindowsYes—
macOSYes—
LinuxYes—
Self-hosted—Yes
Plans published01
Spec score · AI Test Case Generators7.5 (#21)7.8 (#12)

Plans

Casely

No plans published.

Casely pricing →

OpenTest.AI

  • Open source · MITFree

OpenTest.AI pricing →

In their own words

Casely

Casely turns software requirements into QA test cases through a conversation with Claude. It accepts PDF, DOCX, XLSX, TXT, and Markdown files, as well as pasted text. Before writing cases, Casely proposes coverage by module, estimates case counts, flags specification gaps, and waits for approval. Its test design uses equivalence partitioning, boundary value analysis, decision tables, state transitions, and error guessing. The tool can follow the columns, order, tone, and ID scheme of an existing test-case file. Exports include one Excel file and a Markdown file for each case; the Excel output is described as importable into TestRail, Qase, Zephyr, and Xray. When requirements identify API endpoints, it can create a Postman collection with an environment file and run instructions, without embedding credentials. Casely runs with Claude Code, claude.ai, and the Claude desktop app. A browser version with uploads and team review is in early access. The QA skill is free, open source, and MIT-licensed.

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OpenTest.AI

OpenTest.AI is a self-hosted, open-source project for local-first AI quality checks and portable JSON testing formats. Its six skills include automatic bug fixing, deep quality scans, risk analysis, statistical confidence analysis, smart comparisons, and metrics. The skills run locally without an API key, cloud call, or account, and integrate with clients such as Claude Code, Cursor, Codex CLI, GitHub Copilot Chat, Aider, and Continue.dev. jank_deep scans 12 quality dimensions, while jank_risk follows code blast radius, GenAI smells, user frustration, and business risk. jank_confidence uses test-coverage statistics and estimates the cost and time to close gaps. jank_diff compares APIs, documents, or local files while ignoring timestamps, dynamic IDs, and generated metadata noise. The project analyzes screenshots, network and console logs, and DOM or accessibility trees through 33+ AI testing profiles. It supports bug detection, persona feedback, and prioritized test-case generation. Test cases can be replayed by Playwright, Selenium, Cypress, or a human. The MIT plan is free forever.

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