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Can a language model help you pull clean data from messy web pages? The short answer is yes. With Crawl4AI and a small setup, you can turn raw HTML into neat tables. The tool uses an LLM to read page text and fill a schema. It can save time and cut manual work.
This guide shows the main steps to build an LLM web scraping pipeline. You will see how to install the package, run a quick test, and then use an LLM to do structured data extraction. You will also learn a few small configs you can change. All steps are simple. They use plain commands and clear settings.
First, install the tool with pip. Then run the setup helper. Next, test a sample URL to fetch the raw HTML. After that, switch the run mode to the LLM extraction strategy. Provide a Pydantic schema that lists the fields you want. The LLM reads the page and fills that schema.
If you build tools, make reports, or collect lists, this helps. Developers get a faster way to map page text to models. Data analysts get cleaner tables to work with. Hobbyists get a simple path to build a small web scraper without heavy parsing code.
You need a code editor like VS Code. You need Python installed. You need a model provider. You can use OpenRouter or a local LLM such as OLLama. If you use OpenRouter, add the base URL and your API token. For a local model, set the local base URL and model name.
| Setting | Example value | What it does | | --- | --- | --- | | Provider | OpenRouter | Sends requests to a cloud LLM | | Model | gpt-4o or quan-3 | Choose the LLM to run extraction | | Base URL | openrouter.ai/api/v1 | Where to send API calls | | Local model | localhost:11434 (Ollama) | Run a model on your machine | | API token | YOUR_TOKEN | Secure key for the provider | | Browser config | verbose=true, headless=true | Shows logs and hides GUI | | Extraction type | schema | Tells the LLM to fill a Pydantic model | | Chunking | apply_chunking=true, token=1024 | Split large pages for small models |
In the code, you pass a class that uses Pydantic schema. The schema names the fields. Add short descriptions to each field. The LLM uses those descriptions to know what to pull. You can also include an example object to guide the model.
Set the extraction strategy to use the LLM. Turn on verbose logging if you want to see steps in the terminal. Set headless=true so no browser window pops up. Then run the async crawler. The run returns structured objects that match your Pydantic class.
If you use a local model, pick a size that fits your machine. Smaller models can still work. Use chunking to split the page into parts. For example, set chunk size to 1024 tokens. The tool will send parts of the page in order. This helps the model handle long pages.
LLMs are powerful but not perfect. Always do LLM validation. Check a sample of the results. You can run a second model as a judge to verify fields. Or write small rules in code to test values. Do not assume every item is correct without a check.
A small tip: add short descriptions to each schema field. Short notes make the LLM's job easier. Also, keep the prompt simple. Show one sample object. This reduces mistakes.
Ready to try? Go and set up Crawl4AI. Use the provider you prefer. Build a small web scraper tutorial project. Then test, verify, and improve. Start extracting clean tables fast.
Want to try a simple LLM web scraping tool fast? Start by installing Crawl4AI. This guide shows the basic steps. It is short and clear. Go install and test it now.
First, open the project repo and read the quick start. Use a code editor like VS Code and open the terminal. The repo has the exact commands you need. Read the short guide so you know which install and setup commands to run.
Run the pip install command shown in the repo. For most systems use: pip install -U crawl4ai. If you use a virtual environment, activate it first. The install may take about a minute. After installing, run the setup command listed in the repo to finish setup. You may be asked for your sudo password. Be patient and let the setup finish. This step prepares the tool for structured data extraction and Pydantic schema use.
To check the install, run the verification command: crawl4ai-do. The tool will print a success message if things work. If you see an error, re-run the setup or check the repo steps. Once verified, you are ready to try examples that use OpenRouter or local models and to test chunking if needed.
"You should see a message that confirms the installation was successful."
| Action | Command example | Notes |
| --- | --- | --- |
| Clone repo | git clone
Want to turn messy web pages into neat, typed data? Start by planning three things: the data schema, how the browser runs, and which LLM will do the extraction. Keep each part small and clear.
Make a simple Pydantic schema that lists the fields you want. Add short descriptions for each field. The model uses these to return structured data. For example: player name, games played, passing yards. Clear field descriptions help the LLM pick the right text from the page.
Set the browser to headless so it runs in the background. Use verbose logging to see progress in the terminal. Set the extraction strategy to LLM so the model maps page text into your Pydantic schema.
You can pick cloud providers or run a local model. Cloud models are easy. Local models save costs but may need chunking. Tag key names like OpenRouter or Llama so readers spot them fast.
| Provider | Quick note | | --- | --- | | OpenRouter | Use base URL openrouter.ai and an API token. | | Local Llama | Run on localhost (port 11434) and set the base URL accordingly. |
For OpenRouter, set the base URL to the provider's API and add your token. For a local model, use the local server address and the model name. Keep tokens secret and never hard-code them in public code.
Install the tool, then run the example command. Replace the example URL with any page you want to scrape. The tool will fetch the HTML and hand it to the LLM for extraction.
The result shows objects that match your schema. Each object has the fields you defined. Read the values and check types. If numbers are strings, fix the schema or prompt.
Pick a few rows and open the original page. Check the numbers and names. Spot checks help catch mistakes fast. Always verify key entries like player stats.
LLMs can give different answers each run. Add validation steps. You can write a small checker or ask a second LLM to judge results. Use chunking when models are small: split large pages into token-sized pieces to improve accuracy.
Tip: Always validate extracted data. Use small checks or a second LLM as a judge to catch errors early.
Ready to try this? Go set up Crawl4AI with your chosen model and extract clean, structured data. Start now and see how fast your scraper can turn pages into useful data.
Have you ever tried to pull data from a web page and found mistakes? Small tweaks can help a lot. Use clear settings and check the output. These tips come from real runs with Crawl4AI and LLM-powered extraction.
If your model has fewer parameters, it can struggle with big pages. Turn on apply_chunking. Pick a token size like 1024. This splits the page into pieces. Each piece is easier for the model to read. The tool will loop over chunks and merge results. This helps small models like Llama 3.2.2 stay accurate.
Large models are better at understanding messy pages. They can follow long instructions. But they cost more and use more time. Smaller models are cheaper and faster. They work well with chunking and with clear schemas. Choose based on budget and speed needs.
| Aspect | Large LLMs | Small LLMs | | --- | --- | --- | | Cost | Higher | Lower | | Speed | Slower | Faster | | Accuracy on long pages | Better | Needs chunking | | Best use | Complex extraction | Simple or chunked tasks |
Turn on verbose to see what the crawler does. Use headless mode so the browser runs in the background. This shows errors and helps you find bugs. You will see HTML fetches and LLM calls in the logs. That makes debugging fast.
LLMs can make mistakes. Always check a sample of the output. You can also ask another LLM to act as a judge. If values look wrong, re-run with different prompts or chunk sizes. Keep a small script to compare new runs with old ones. Re-run if many rows change.
LLM web scraping works well when pages have tables or repeated blocks. Think sports stats, product lists, or score tables. Using a clear Pydantic schema helps the model map fields to types. The LLM fills the schema from the page text.
This approach fits people who build data pipelines. ML engineers and data scientists can use it to gather training data. Developers can add it to ETL jobs. It is also handy for quick prototypes and testing ideas.
Remember that LLMs are nondeterministic. They may return different values on each run. Dynamic sites that load with JavaScript need a real browser. Also watch API rate limits from model providers like OpenRouter. Plan retries and backoff logic.
Start by cloning the sample repo. Open a terminal and run: pip install -U crawl4ai. Then run the setup command. Use crawl4ai-do to check the install. Try the example that pulls a page HTML. Replace the URL with a page you like. For LLM extraction, set the run config to use the LLM strategy and point to your model provider.
Define a clear Pydantic schema for the fields you want. Add short descriptions for each field. Put the schema into the extraction config. If you use a local model like Ollama, change the base URL to localhost:11434. If you use OpenRouter, add your model name and API token. Try chunking with apply_chunking=true and token_size=1024 if your model is small.
Now is a good time to try it. Clone the code. Install Crawl4AI. Plug in your API key for OpenRouter or other providers. Run the demo that extracts NFL stats to a schema. Check the results and tweak token sizes, verbose logging, and chunking. That will help you build a solid LLM web scraper.
