Installation & Setup (2023 Edition)

1. Basic Installation

pip install crawl4ai

This installs the core Crawl4AI library along with essential dependencies. No advanced features (like transformers or PyTorch) are included yet.

2. Initial Setup & Diagnostics

2.1 Run the Setup Command

After installing, call:

crawl4ai-setup

What does it do? - Installs or updates required Playwright browsers (Chromium, Firefox, etc.) - Performs OS-level checks (e.g., missing libs on Linux) - Confirms your environment is ready to crawl

2.2 Diagnostics

Optionally, you can run diagnostics to confirm everything is functioning:

crawl4ai-doctor

This command attempts to: - Check Python version compatibility - Verify Playwright installation - Inspect environment variables or library conflicts

If any issues arise, follow its suggestions (e.g., installing additional system packages) and re-run crawl4ai-setup.

3. Verifying Installation: A Simple Crawl (Skip this step if you already run `crawl4ai-doctor`)

Below is a minimal Python script demonstrating a basic crawl. It uses our new BrowserConfig and CrawlerRunConfig for clarity, though no custom settings are passed in this example:

import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig

async def main():
    async with AsyncWebCrawler() as crawler:
        result = await crawler.arun(
            url="https://www.example.com",
        )
        print(result.markdown[:300])  # Show the first 300 characters of extracted text

if __name__ == "__main__":
    asyncio.run(main())

Expected outcome: - A headless browser session loads example.com - Crawl4AI returns ~300 characters of markdown.
If errors occur, rerun crawl4ai-doctor or manually ensure Playwright is installed correctly.

4. Advanced Installation (Optional)

Warning: Only install these if you truly need them. They bring in larger dependencies, including big models, which can increase disk usage and memory load significantly.

4.1 Torch, Transformers, or All

Text Clustering (Torch)
```
pip install crawl4ai[torch]
crawl4ai-setup
```
Installs PyTorch-based features (e.g., cosine similarity or advanced semantic chunking).
Transformers
```
pip install crawl4ai[transformer]
crawl4ai-setup
```
Adds Hugging Face-based summarization or generation strategies.

All Features

pip install crawl4ai[all]
crawl4ai-setup

(Optional) Pre-Fetching Models

crawl4ai-download-models

This step caches large models locally (if needed). Only do this if your workflow requires them.

5. Docker (Experimental)

We provide a temporary Docker approach for testing. It’s not stable and may break with future releases. We plan a major Docker revamp in a future stable version, 2025 Q1. If you still want to try:

docker pull unclecode/crawl4ai:basic
docker run -p 11235:11235 unclecode/crawl4ai:basic

You can then make POST requests to http://localhost:11235/crawl to perform crawls. Production usage is discouraged until our new Docker approach is ready (planned in Jan or Feb 2025).

6. Local Server Mode (Legacy)

Some older docs mention running Crawl4AI as a local server. This approach has been partially replaced by the new Docker-based prototype and upcoming stable server release. You can experiment, but expect major changes. Official local server instructions will arrive once the new Docker architecture is finalized.

Summary

1. Install with pip install crawl4ai and run crawl4ai-setup. 2. Diagnose with crawl4ai-doctor if you see errors. 3. Verify by crawling example.com with minimal BrowserConfig + CrawlerRunConfig. 4. Advanced features (Torch, Transformers) are optional—avoid them if you don’t need them (they significantly increase resource usage). 5. Docker is experimental—use at your own risk until the stable version is released. 6. Local server references in older docs are largely deprecated; a new solution is in progress.

Got questions? Check GitHub issues for updates or ask the community!