NewsAPI Python API Docs | dltHub

News API is a simple HTTP REST API for searching and retrieving live articles from news sources and blogs. The REST API base URL is https://newsapi.org/v2 and all requests require an apiKey (can be query param or X-Api-Key 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 NewsAPI data in under 10 minutes.

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What data can I load from NewsAPI?

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

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How do I authenticate with the NewsAPI API?

Authentication uses a single API key. Provide it either as the apiKey query parameter or in the X-Api-Key HTTP request header on every request.

1. Get your credentials

1. Sign up or log in at https://newsapi.org/. 2) Open your dashboard. 3) Copy the displayed API key labeled "API key". 4) Use it as the apiKey query parameter or X-Api-Key header in requests.

2. Add them to .dlt/secrets.toml

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[sources.news_api_source]
api_key = "YOUR_NEWSAPI_API_KEY"

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 NewsAPI 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 news_api_pipeline.py

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

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Pipeline news_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset news_api_data
The duckdb destination used duckdb:/news_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 news_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 everything and top_headlines from the NewsAPI 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 news_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://newsapi.org/v2",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "everything", "endpoint": {"path": "v2/everything", "data_selector": "articles"}},
            {"name": "top_headlines", "endpoint": {"path": "v2/top-headlines", "data_selector": "articles"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="news_api_pipeline",
        destination="duckdb",
        dataset_name="news_api_data",
    )
    load_info = pipeline.run(news_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("news_api_pipeline").dataset()
sessions_df = data.everything.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM news_api_data.everything LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("news_api_pipeline").dataset()
data.everything.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 NewsAPI 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 status "error" with code "apiKeyMissing" or "apiKeyInvalid", verify you supplied your API key either as the apiKey query parameter or in the X-Api-Key header and that the key is active in your NewsAPI dashboard.

Rate limits and plan limits

NewsAPI enforces plan-based usage limits and result windowing (older articles limited by plan). If you hit rate limits or get fewer results than expected, check your plan and usage in the dashboard; responses may indicate the limit in the message.

Pagination quirks

Endpoints return totalResults and support page and pageSize (max 100). Use page and pageSize to iterate. Note: totalResults may be capped by plan and not reflect full historical counts.

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