Scrapingbee Python API Docs | dltHub

ScrapingBee is a web scraping API that returns rendered HTML or structured JSON for target web pages via a simple HTTP API. The REST API base URL is https://app.scrapingbee.com/api/v1 and All requests require an api_key query parameter (API key) for authentication..

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 Scrapingbee data in under 10 minutes.

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What data can I load from Scrapingbee?

Here are some of the endpoints you can load from Scrapingbee:

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How do I authenticate with the Scrapingbee API?

Authentication is via an api_key parameter (query string) or an "X-API-KEY" header; include the key with every request (e.g. ?api_key=YOUR_API_KEY).

1. Get your credentials

1. Sign up or sign in at https://app.scrapingbee.com/. 2) Open Dashboard → Account → Manage API key (or visit https://dashboard.scrapingbee.com/account/manage/api_key). 3) Copy the API key value and store it securely.

2. Add them to .dlt/secrets.toml

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[sources.scrapingbee_source]
api_key = "your_api_key_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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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

1. Install the dlt AI Workbench:

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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:

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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 Scrapingbee 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:

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python scrapingbee_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline scrapingbee_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.

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Python pipeline example

This example loads html_api and json_response from the Scrapingbee API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def scrapingbee_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://app.scrapingbee.com/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "html_api", "endpoint": {"path": "api/v1"}},
            {"name": "json_response", "endpoint": {"path": "api/v1", "data_selector": "body"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="scrapingbee_pipeline",
        destination="duckdb",
        dataset_name="scrapingbee_data",
    )
    load_info = pipeline.run(scrapingbee_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.

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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):

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import dlt

data = dlt.pipeline("scrapingbee_pipeline").dataset()
sessions_df = data.html_api.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM scrapingbee_data.html_api LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("scrapingbee_pipeline").dataset()
data.html_api.df().head()

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

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What destinations can I load Scrapingbee 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.

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Troubleshooting

Authentication failures

If you receive 401 or a message about missing API key, ensure api_key is provided (query string ?api_key=YOUR_API_KEY) or correct key in header/SDK. Verify no accidental URL encoding removed the key.

Rate limits and concurrency

Requests are limited by plan‑based concurrency and available credits. If you hit concurrency or quota limits, you may see errors or rejected requests; upgrade plan or reduce parallel requests.

URL encoding and unknown arguments

Always URL‑encode the target URL. If the target URL contains query params that are unencoded, ScrapingBee may interpret them as unknown arguments and return an "Unknown arguments" error. Use proper encoding (e.g. urlencode or encodeURIComponent).

JSON response and file limits

When using json_response=True, the response body may include base64‑encoded content for files (images, pdfs). There is a 2 MB limit per request for downloaded files — large responses may be truncated or error.

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

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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.

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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