Search API Python API Docs | dltHub

Search API is a real-time, structured JSON API that provides programmatic access to Google Trends data including timeseries (interest over time), geo maps (interest by region), related queries, related topics, trending-now (real-time) searches, trending-now news, and autocomplete suggestions. The REST API base URL is https://www.searchapi.io/api/v1 and all requests require an API key (can be sent as query param or Bearer token).

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 Search API data in under 10 minutes.

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What data can I load from Search API?

Here are some of the endpoints you can load from Search API:

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How do I authenticate with the Search API API?

Provide your api_key either as a query parameter (?api_key=YOUR_API_KEY) or in the Authorization header as Bearer YOUR_API_KEY. Requests without a valid api_key return authentication errors.

1. Get your credentials

1. Sign up / log in at https://www.searchapi.io. 2) Open Dashboard / API Keys or Account -> obtain your API key. 3) Copy the API key and use it in requests as query param api_key or as Authorization: Bearer <API_KEY>.

2. Add them to .dlt/secrets.toml

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[sources.search_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 Search 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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python search_api_pipeline.py

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

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Pipeline search_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset search_api_data
The duckdb destination used duckdb:/search_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 search_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 google_trends_search and google_trends_trending_now from the Search 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 search_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.searchapi.io/api/v1",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "google_trends_search", "endpoint": {"path": "api/v1/search?engine=google_trends", "data_selector": "interest_over_time (for TIMESERIES responses) or interest_by_region or related_queries or related_topics depending on data_type"}},
            {"name": "google_trends_trending_now", "endpoint": {"path": "api/v1/search?engine=google_trends_trending_now", "data_selector": "trends"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="search_api_pipeline",
        destination="duckdb",
        dataset_name="search_api_data",
    )
    load_info = pipeline.run(search_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("search_api_pipeline").dataset()
sessions_df = data.google_trends_trending_now.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM search_api_data.google_trends_trending_now LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("search_api_pipeline").dataset()
data.google_trends_trending_now.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 Search 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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Troubleshooting

Authentication failures

If you receive 401 or messages indicating missing/invalid API key, ensure your api_key is included either as a query parameter (api_key=YOUR_API_KEY) or Authorization: Bearer YOUR_API_KEY. Confirm the key in your dashboard and that it is not expired or revoked.

Rate limits and quotas

SearchApi enforces request quotas per account. If you hit limits you may receive HTTP 429 responses. Implement exponential backoff and retries. Consider upgrading plan or batching queries.

Missing/empty data (zero results)

Some combinations (e.g., hourly ranges beyond allowed window, empty q with data_type=TIMESERIES when cat=0) are invalid or produce empty results. Validate parameters: TIMESERIES requires q unless cat !=0; hourly ranges limited to past week.

Pagination / large responses

Most Google Trends endpoints return arrays inside named keys (e.g., timeline_data, interest_by_region, trends). Use the JSON URL from search_metadata.json_url to fetch saved results if needed. For very large requests, reduce time window or split queries.

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