Scrapingdog Google AI Overview API Python API Docs | dltHub

Scrapingdog Google AI Overview API allows developers to fetch Google AI Overview results by passing a URL obtained from the main Google Search API response. The REST API base URL is https://api.scrapingdog.com/google/ai_overview and all requests require an 'api_key' parameter in the query string.

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 add "dlt[hub]" and start loading Scrapingdog Google AI Overview API data in under 10 minutes.

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What data can I load from Scrapingdog Google AI Overview API?

Here are some of the endpoints you can load from Scrapingdog Google AI Overview API:

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How do I authenticate with the Scrapingdog Google AI Overview API API?

Authentication is performed by passing a required 'api_key' parameter in the query string of the request URL. No additional headers are required.

1. Get your credentials

1. Navigate to the Scrapingdog website (scrapingdog.com) and click on the 'Sign Up' or 'Register' button to create a free account if you have not already done so. 2. Once registered, log in to your account. 3. Navigate to the dashboard page (https://api.scrapingdog.com/dashboard) to view and copy your unique personal API key.

2. Add them to .dlt/secrets.toml

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[sources.scrapingdog_google_ai_overview_api_source]
scrapingdog_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Scrapingdog Google AI Overview API 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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uv run python scrapingdog_google_ai_overview_api_pipeline.py

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

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Pipeline scrapingdog_google_ai_overview_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset scrapingdog_google_ai_overview_api_data
The duckdb destination used duckdb:/scrapingdog_google_ai_overview_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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uv run dlthub 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 https://api.scrapingdog.com/google and https://api.scrapingdog.com/google/ai_overview from the Scrapingdog Google AI Overview API 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 scrapingdog_google_ai_overview_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.scrapingdog.com/google/ai_overview",
            "auth": {"type": "api_key", "api_key": api_key},
        },
        "resources": [
            {"name": "google_search", "endpoint": {"path": "google", "data_selector": "organic_results"}},
            {"name": "account", "endpoint": {"path": "account"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="scrapingdog_google_ai_overview_api_pipeline",
        destination="duckdb",
        dataset_name="scrapingdog_google_ai_overview_api_data",
    )
    load_info = pipeline.run(scrapingdog_google_ai_overview_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("scrapingdog_google_ai_overview_api_pipeline").dataset()
sessions_df = data.google_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM scrapingdog_google_ai_overview_api_data.google_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("scrapingdog_google_ai_overview_api_pipeline").dataset()
data.google_search.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 Scrapingdog Google AI Overview API 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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Next steps

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

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

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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