Bricks, a home and the neighbourhood aunty, then an agent that drives a real browser

mkdir langchain && cd langchain
pip install virtualenv
python -m venv venv
source venv/bin/activate          # Windows: venv\Scripts\activate

pip install langchain langchain-groq langchain-google-genai langchain-ollama python-dotenv pydantic

GROQ_API_KEY=...
LLM_MODEL=openai/gpt-oss-120b
GEMINI_LLM_MODEL=google_genai:gemini-flash-lite-latest

from dotenv import load_dotenv
from langchain_groq import ChatGroq
import os

load_dotenv()

def main():
    llm = ChatGroq(model=os.getenv("LLM_MODEL"), temperature=1)
    query = input(" Enter the question ")
    response = llm.invoke(query)
    print(response.content)

if __name__ == "__main__":
    main()

from dotenv import load_dotenv
from langchain.agents import create_agent
import os

load_dotenv()

def main():
    agent = create_agent(model=os.environ["GEMINI_LLM_MODEL"])
    query = input("Ask your question: ")
    result = agent.invoke({"messages": [("user", query)]})
    print(result["messages"][-1].text)

agent = create_agent(
    model="google_genai:gemini-flash-lite-latest",
    system_prompt=(
        "You are a helpful AI assistant specialized in automation testing "
        "and software development. Be clear, concise, and always think step by step."
    ),
)

result = agent.invoke({"messages": [{"role": "user", "content": "Who are you?"}]})
print(result["messages"][-1].content)

import asyncio, time
from langchain.agents import create_agent

agent = create_agent(
    model="google_genai:gemini-flash-lite-latest",
    system_prompt="You are an experienced software testing instructor. Explain each concept in 10 bullet points",
)

questions = [
    {"id": 0, "topic": "LLM Eval",           "prompt": "What is llm Eval?"},
    {"id": 1, "topic": "Smoke Testing",      "prompt": "What is smoke testing?"},
    {"id": 2, "topic": "Sanity Testing",     "prompt": "What is sanity testing?"},
    {"id": 3, "topic": "Regression Testing", "prompt": "What is regression testing?"},
]

async def main():
    start = time.perf_counter()
    # asyncio.gather runs every agent.ainvoke() call CONCURRENTLY
    results = await asyncio.gather(*[
        agent.ainvoke({"messages": [{"role": "user", "content": q["prompt"]}]})
        for q in questions
    ])
    # results come back in the same order as the questions list
    for q, result in zip(questions, results):
        print(f"{q['id']}. {q['topic']}")
        print(result["messages"][-1].content)

asyncio.run(main())

from langchain.tools import tool

@tool
def qa_metric_calculator(expression: str) -> str:
    """Calculate a QA metric from an arithmetic expression.

    Use this for pass rate, defect density, automation coverage, defect leakage
    or execution time. Input must be plain arithmetic with no words, for example
    '(438/500)*100' for a pass rate or '18/12' for defects per KLOC.
    """
    result = _safe_eval(ast.parse(expression, mode="eval").body)
    return f"{round(result, 2)}"

agent = create_agent(
    model="google_genai:gemini-flash-lite-latest",
    tools=[qa_metric_calculator],
    system_prompt=(
        "You are a QA metrics assistant for a test automation team. "
        "Use the qa_metric_calculator tool whenever a number must be computed. "
        "State the formula you used, then the result with its unit."
    ),
)

from typing import Literal
from pydantic import BaseModel
from langchain.agents import create_agent

class TestCase(BaseModel):
    title: str
    description: str
    steps: list[str]
    expected_result: str
    priority: Literal["High", "Medium", "Low"]
    tags: list[str]

class TestCaseList(BaseModel):
    # Tip: always wrap arrays inside an object (better compatibility, especially with Anthropic)
    test_cases: list[TestCase]

agent = create_agent(
    model="google_genai:gemini-flash-lite-latest",
    system_prompt=("You are a senior automation testing engineer. Always return test cases "
                   "in the exact structured JSON format requested."),
    response_format=TestCaseList,
)

result = agent.invoke({"messages": [{"role": "user", "content":
    "Generate 2 detailed test cases for this user story: As a logged-in user, "
    "I want to add items to my shopping cart so that I can purchase them later."}]})

for tc in result["structured_response"].test_cases:
    print(tc.model_dump_json(indent=2))

llm = ChatGroq(model=os.getenv("LLM_MODEL"), temperature=1)

SYSTEM_PROMPT = """
You are an automation testing agent that controls a real browser using Playwright.

Rules:

1. Always start by launching the browser before performing any action.
2. Perform the actions in the order they are requested.
3. Use clear CSS selectors to interact with elements on the page.
4. Take a screenshot before closing the browser to capture the final state.
5. At the end, report every step you took and whether the test PASSED or FAILED."""

TASK = """ Test the login functionality on https://app.thetestingacademy.com/playwright/ttacart/

1. Launch the browser
2. Navigate to the login page
3. Enter the username "standard_user"
4. Enter the password "tta_secret"
5. Click the login button
6. Close the browser
"""

async def main():
    agent = create_agent(model=llm, tools=PLAYWRIGHT_TOOLS, system_prompt=SYSTEM_PROMPT)
    # Playwright's async API needs ainvoke: every tool call runs on the same event loop
    result = await agent.ainvoke({"messages": [{"role": "user", "content": TASK}]})

    print("\n===== Steps taken =====")
    for msg in result["messages"]:
        if msg.type == "ai" and msg.tool_calls:
            for call in msg.tool_calls:
                print(f"-> {call['name']}({call['args']})")
        elif msg.type == "tool":
            print(f"   {msg.content}")

asyncio.run(main())