ScraperAPI Python API Docs | dltHub

ScraperAPI is a web scraping proxy and data extraction platform that returns page HTML or structured JSON/CSV from supported sites. The REST API base URL is https://api.scraperapi.com and all requests require an API key (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 ScraperAPI data in under 10 minutes.

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What data can I load from ScraperAPI?

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

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How do I authenticate with the ScraperAPI API?

Authentication is performed via an api_key query parameter (api_key=YOUR_KEY) included on every request to ScraperAPI endpoints. Some management/other APIs may accept api_key in the request body for POST requests (see crawler/docs).

1. Get your credentials

1. Sign up or log in at https://www.scraperapi.com/ 2) Open Dashboard -> API Key / Account or Settings 3) Copy the displayed API Key to use in requests

2. Add them to .dlt/secrets.toml

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[sources.scraper_api_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 ScraperAPI 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 scraper_api_pipeline.py

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

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Pipeline scraper_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset scraper_api_data
The duckdb destination used duckdb:/scraper_api.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 scraper_api_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 sync_api and structured_base from the ScraperAPI 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 scraper_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.scraperapi.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "sync_api", "endpoint": {"path": "api.scraperapi.com"}},
            {"name": "structured_base", "endpoint": {"path": "api.scraperapi.com/structured/"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="scraper_api_pipeline",
        destination="duckdb",
        dataset_name="scraper_api_data",
    )
    load_info = pipeline.run(scraper_api_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("scraper_api_pipeline").dataset()
sessions_df = data.sync_api.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM scraper_api_data.sync_api LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("scraper_api_pipeline").dataset()
data.sync_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 ScraperAPI 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 requests return 401/403 or the request is rejected, verify you included the api_key query parameter exactly as api_key=YOUR_KEY. Ensure the key is active in Dashboard and not expired or revoked.

Rate limits / quota errors

ScraperAPI enforces plan quotas and concurrent request limits. If you receive errors indicating rate limiting or see many failed requests, reduce concurrency and check Dashboard usage/credits. Use session_number and caching features to reduce cost.

Timeouts and large responses

ScraperAPI recommends a client timeout of ~70 seconds. Requests for very large files (>50MB) are limited and may fail. Use Async API or DataPipeline for large crawls.

Structured endpoints / varying JSON shapes

Structured endpoints are tailored per site and return JSON/CSV with a site-specific structure. Inspect example responses in the docs for the particular structured endpoint you plan to use — do not assume a universal top-level key. If integrating into dlt, fetch a sample response and confirm the exact array key to use as the data selector.

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