Web Scraper Cloud Python API Docs | dltHub

Web Scraper Cloud is a cloud‑based service that lets users run web scraping projects and retrieve extracted data via a REST API. The REST API base URL is https://api.webscraper.io/api/v1 and All requests require an API token passed as the api_token query parameter or as a Bearer token in the Authorization header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading Web Scraper Cloud data in under 10 minutes.

---

What data can I load from Web Scraper Cloud?

Here are some of the endpoints you can load from Web Scraper Cloud:

---

How do I authenticate with the Web Scraper Cloud API?

Include the API token either as the api_token query parameter in the URL or as an Authorization: Bearer <TOKEN> header for every request.

1. Get your credentials

1. Log in to your Web Scraper Cloud account.
2. Navigate to the "API" section (URL: https://cloud.webscraper.io/api).
3. Click "Generate new token" or copy the existing token displayed.
4. Store the token securely for use in dlt configuration.

2. Add them to .dlt/secrets.toml

Copy
[sources.web_scraper_cloud_source]
api_token = "your_api_token_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

Copy
dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the Web Scraper Cloud API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

Copy
python web_scraper_cloud_pipeline.py

If everything is configured correctly, you'll see output like this:

Copy
Pipeline web_scraper_cloud_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset web_scraper_cloud_data
The duckdb destination used duckdb:/web_scraper_cloud.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline web_scraper_cloud_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads sitemaps and scraping_jobs from the Web Scraper Cloud API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def web_scraper_cloud_source(api_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.webscraper.io/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_token,
            },
        },
        "resources": [
            {"name": "sitemaps", "endpoint": {"path": "sitemaps?api_token=<YOUR API TOKEN>", "data_selector": "data"}},
            {"name": "scraping_jobs", "endpoint": {"path": "scraping-jobs?api_token=<YOUR API TOKEN>", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="web_scraper_cloud_pipeline",
        destination="duckdb",
        dataset_name="web_scraper_cloud_data",
    )
    load_info = pipeline.run(web_scraper_cloud_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

---

How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("web_scraper_cloud_pipeline").dataset()
sessions_df = data.sitemaps.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM web_scraper_cloud_data.sitemaps LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("web_scraper_cloud_pipeline").dataset()
data.sitemaps.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load Web Scraper Cloud data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

---

Troubleshooting

Authentication errors

• Symptoms: HTTP 401 Unauthorized or 403 Forbidden.
• Cause: Missing, expired, or incorrectly formatted api_token.
• Resolution: Verify that the token from the API page is correct and included either as api_token query param or in the Authorization: Bearer header.

Rate limiting

• Symptoms: HTTP 429 Too Many Requests.
• Cause: Exceeding the default limit of 200 calls per 15 minutes.
• Resolution: Observe the response headers X-RateLimit-Limit, X-RateLimit-Remaining, and X-RateLimit-Reset and throttle requests accordingly.

Pagination / full sync

• Symptoms: Missing records when expecting pagination.
• Cause: The API does not support pagination for certain endpoints; it returns the full dataset in a single response.
• Resolution: Treat the response as a complete snapshot and design pipelines for full sync rather than incremental pagination.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

Copy
dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack