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Playwright TypeScript & Appium Python Automation
Bestseller
Rating: 4.4 out of 5(128 ratings)
1,736 students

Playwright TypeScript & Appium Python Automation

Playwright TypeScript web & API, Appium 3 Python mobile, Page Objects, Pytest, MCP & AI for QA
Created byLucky Trainings
Last updated 9/2026
English
English [Auto],Spanish [Auto],

What you'll learn

  • Understand the core concepts of Artificial Intelligence, LLMs, Generative AI, RAG, Memory, Context, and AI Agents
  • Use AI to generate Playwright code, test cases, and selectors
  • Generate Appium test cases with AI assistance
  • Create automation workflows using n8n to integrate AI into testing
  • Build smart, scalable workflows that combine AI + automation frameworks

Course content

17 sections362 lectures44h 9m total length
  • Introduction1:29

    Learn AI fundamentals and automation concepts to generate web and mobile automation scripts with Playwright (TypeScript) and APM Python, guided by Surendra.

  • Overview on AI Part 14:29

    Artificial intelligence is a computer system that can think, learn, and decide like a human, trained on data to perform tasks such as writing emails and automation tests.

  • Playwright Course Material0:30

    Access the complete course material, including the full folder structure and test scenarios used in TypeScript Playwright lectures, downloadable for immediate use.

  • Appium Course Material1:43

    Explore the python-based test automation material for android and ios using an APM framework, including source code, zip files, page factory and page object models, utilities, and test scenarios.

  • Overview on AI Part 29:28

    Explore how humans collaborate with ai to boost efficiency and reliability in software testing and development. Learn how human-in-the-loop practices mitigate ai risks with practical, real-world examples.

  • Overview on LLM6:35

    Understand how a large language model, a human-brain like AI trained on vast data, translates, summarizes, answers, and generates content; it differs from Google, powered by backend programs.

  • Overview on RAG2:14

    Explore retrieval augmented generation (rag) and how it augments LLMs with a vector database and embedded information from a knowledge base to produce accurate responses without full retraining.

  • Overview on Generative AI3:15

    Discover how generative ai creates new content beyond existing data, with examples like generating a selenium Java program, consolidating loan documents, and producing text, images, audio, or video.

  • Overview on Memory5:14

    Explore how AI memory stores, retains, and retrieves past interactions to enable meaningful conversations, distinguishing short-term memory within a session from long-term memory across sessions.

  • Overview on AI Agent7:52

    Discover how an ai agent, a software program, uses tools to achieve a goal, can interact with other agents, perceive the environment, reason, plan, act, and adjust based on results.

  • Overview on LangChain & LangGraph4:55

    Explore LangChain, an open-source framework connecting LLMs to external data sources, and LangGraph, a line graph style graphical workflow with nodes, edges, states, and checkpoints for AI agents.

  • Overview on MCP4:39

    Learn how the model context protocol (MCP) standardizes interactions between AI models and external tools, enabling tool discovery, execution, and reuse across APIs, databases, and services.

  • Overview on Human In the loop , Hallucination & Guardrails8:24

    Learn how human in the loop governs AI decisions, with approvals for budget-sensitive actions, and how guardrails and prompts prevent hallucination.

  • Overview on Fine-Tuning3:55

    Explore how fine tuning tailors an AI agent's interactions for customers, branch staff, and officials, and distinguish it from rag that uses external knowledge.

  • Overview on Context Part 12:55

    Learn how providing precise context to language models and LMS improves output accuracy by guiding the model with defined information and rules.

  • Overview on Prompts5:34

    Learn the fundamentals of prompt engineering by providing clear context and precise inputs to guide responses and reduce hallucinations with practical examples.

  • Overview on Context Part 27:39

    Learn to set an LLM context by defining a role, rules, and outputs, then pass it with a context file and UI mockups to generate manual or automation test cases.

  • Overview on Context Part 33:20

    Learn to use a manual context file to guide a generic login flow (positive and negative tests) with ChatGPT, noting no acceptance criteria for the demo app.

  • Overview on Prompts Part 27:13

    Explore zero-shot, one-shot, few-shot, and chain-of-thought prompts to craft clear instructions for automation testing and context files guiding LLMs in web and mobile automation.

  • ChatGPT vs Copilot vs CURSOR5:00

    Compare ChatGPT, Copilot, and Cursor for automation development, highlighting ChatGPT's general AI and Copilot/Cursor's code-aware support for Playwright and Appium.

  • Overview on OpenAI3:08

    OpenAI is a research and development company aiming to ensure artificial general intelligence benefits humanity. It develops principles and protocols for building AI, powering GPT models, ChatGPT, Copilot, and Dall-E.

  • Overview on AI Models3:56

    AI models are algorithms trained on data to recognize patterns and generate outputs, with GPT for text and DALL-E for images; LLMs are specialized AI models focused on human-like text.

  • Overview on n8n workflow6:30

    Explore n8n as a workflow automation platform that combines AI capabilities to build no-code AI agents, create trials, and design automated workflows like email reminders and data processing.

  • Generate API Key in OpenAI1:49

    Sign up on platform.openai.com, create and copy a new secret API key, and use that key to connect OpenAI for building AI agents.

  • Create workflow in n8n10:40

    Build an AI powered workflow in n8n that connects Gmail and Google Drive, reads a test data sheet, and sends an email when Sam is found.

  • Create workflow in n8n Part 27:42

    Build a dynamic email workflow in n8n that reads a Google sheet, checks for a user, and lets an AI agent decide the recipient before sending.

  • Create workflow in n8n Part 39:23

    Build an n8n workflow that reads data from Google Sheets (sheet two), checks status values, and automatically emails users when status is S to initiate background verification.

  • Create account in JIRA for our testing purpose7:10

    Explore creating a Jira Cloud account, generating an API token, and integrating Zephyr test management to automate test case and defect creation from Excel-driven workflows.

  • n8n workflow for creating Bugs in JIRA Part 15:58

    Follow an n8n workflow to create Jira issues from an Excel data sheet in a scrum setup, configuring credentials (email, API token, domain) and troubleshooting connectivity.

  • n8n workflow for creating Bugs in JIRA Part 25:04

    Configure cloud credentials and pass the access key, email, and URL to establish a connection, then automatically create Jira bugs from new defects in an n8n workflow.

  • Create Public Chat in n8n workflow6:02

    Publish a public chat in an n8n workflow with a url, connect to an ai model and memory, and use sheet three to create defects with status and defect columns.

  • Overview on OpenAI Tokens4:32

    Learn how OpenAI tokens define input cost and processing, with tokens as word parts, roughly four characters per token, and how to manage prompts and budgets on trial accounts.

  • CURSOR - Create a Chrome Extension for Record & Playback20:27

    Learn how Cursor AI helps you generate a Chrome extension that records and replays browser actions, producing Playwright or Selenium scripts, with a human in the loop workflow.

  • CURSOR - Create a Chrome Extension for Record & Playback Part 22:11

    Use cursor AI to generate a Chrome extension for record and playback, producing Playwright TypeScript or Selenium scripts that can be downloaded and tested.

  • CURSOR - Create an OTP Shield Mobile APP21:15

    Develop an OTP shield mobile app that monitors text messages, calls, and WhatsApp for OTP requests, warns the user with a dismiss option, and blocks APK files.

  • Overview on GPT4ALL with example14:33

    Discover gpt4all, an open source ecosystem that runs LLMs privately on your computer with offline capability, a desktop app for Windows, macOS, and Linux, and zero cloud dependency.

  • Overview on Custom Agent.md file7:08

    Explore how to build local agents with agent.md and skills.md to generate manual test cases and automation scripts from story or test case IDs, integrating with Azure or Jira.

  • Creating Manual Testcase generator Agent part 113:52

    Create a custom manual test case generator agent that fetches Azure DevOps stories, generates test cases per defined rules, saves drafts, and optionally uploads to Azure DevOps user stories.

  • Creating Manual Testcase generator Agent part 210:42

    Build a python-based agent that fetches user story details from Azure Dev Apps, prints title, description, acceptance criteria, and saves UI mockups as screenshots to generate manual test cases.

  • Creating Manual Testcase generator Agent part 314:33

    Learn how to configure a manual test case generator agent to create end-to-end, priority-based test scenarios with clear steps and expected results, saved to story id md drafts in ui.

  • Create an Playwright Agent12:42

    Create an automation test case agent that fetches Azure test steps and generates a Playwright JavaScript automation script within a project, using skill.md and an automation tab.

Requirements

  • Basic understanding of software testing
  • No AI knowledge required — everything is explained from scratch
  • Laptop with internet connection

Description

** Complete path: Playwright TypeScript (Web & API) + Appium 3 Python (Mobile) + AI **


Learn modern web and mobile automation in one course — Playwright with TypeScript for web & API testing, Appium 3 with Python for Android/iOS mobile automation, plus AI and MCP workflows for QA.


This Playwright and Appium course is built for manual testers, beginners, and QA engineers who want practical full-stack automation skills across web and mobile.


What you will learn in this course:

- AI fundamentals for modern QA workflows

- TypeScript basics required for Playwright automation

- Playwright TypeScript web automation (locators, assertions, UI interactions)

- Playwright API testing, Page Object Model, Git & GitHub Actions

- Playwright MCP and AI-assisted testing concepts

- Python fundamentals required for Appium automation

- Appium 3 setup and configuration for mobile testing

- Android/iOS mobile automation using Appium with Python

- Framework concepts for scalable web and mobile test suites


Keywords covered in this course:

Playwright TypeScript, Playwright automation, Playwright API testing, Appium Python, Appium 3, Appium mobile automation, web and mobile automation, Python automation testing, Page Object Model, Pytest, MCP, AI for QA, Android automation, iOS automation.


This course combines Playwright TypeScript for web/API and Appium 3 Python for mobile so you can build end-to-end automation skills for real projects and interviews.


If you are searching for Playwright TypeScript, Appium Python, Appium 3, or web and mobile automation training in one course, this course is for you.

Who this course is for:

  • Manual testers who want to upgrade to automation
  • Anyone interested in LLMs, RAG, AI Agents, n8n, Cursor, OpenAI
  • Students or professionals planning to shift into AI-driven QA