A Cursor-powered AI development environment with advanced agentic capabilities.
-
Activate the virtual environment:
# On Windows venv\Scripts\activate # On macOS/Linux source venv/bin/activate
-
Configure your environment:
- Copy
.env.example
to.env
if you haven't already - Add your API keys in
.env
(optional)
- Copy
Your project includes several powerful tools in the tools/
directory:
from tools.llm_api import query_llm
# Use LLM for assistance
response = query_llm(
"Your question here",
provider="anthropic" # Options: openai, anthropic, azure_openai, deepseek, gemini
)
print(response)
from tools.web_scraper import scrape_urls
# Scrape web content
results = scrape_urls(["https://example.com"], max_concurrent=3)
from tools.search_engine import search
# Search the web
results = search("your search keywords")
from tools.screenshot_utils import take_screenshot_sync
from tools.llm_api import query_llm
# Take and analyze screenshots
screenshot_path = take_screenshot_sync('https://example.com', 'screenshot.png')
analysis = query_llm(
"Describe this webpage",
provider="openai",
image_path=screenshot_path
)
Note: When you first use the screenshot verification feature, Playwright browsers will be installed automatically.
This project uses .cursorrules
to configure the AI assistant. The assistant can:
- Help with coding tasks
- Verify screenshots
- Perform web searches
- Analyze images and code
Configure these in your .env
file:
LLM_API_KEY
: Your LLM API key (optional)AZURE_OPENAI_API_KEY
: Azure OpenAI API key (optional)AZURE_OPENAI_ENDPOINT
: Azure OpenAI endpoint (optional)AZURE_OPENAI_MODEL_DEPLOYMENT
: Azure OpenAI model deployment name (optional)
Note: Basic functionality works without API keys. Advanced features (like multimodal analysis) require appropriate API keys.
.devcontainer/
: VS Code development container configuration.vscode.example/
: Recommended VS Code settings.github/
: CI/CD workflows
MIT License