Wikimedia Python API Docs | dltHub

The Wikimedia REST API offers access to Wikimedia's content and metadata in machine-readable formats. The REST API base URL is https://en.wikipedia.org/api/rest_v1/ and No authentication is strictly required, but a unique User-Agent or Api-User-Agent header is recommended..

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 Wikimedia data in under 10 minutes.

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What data can I load from Wikimedia?

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

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How do I authenticate with the Wikimedia API?

No explicit authentication token is required. However, all requests should include a unique User-Agent or Api-User-Agent header for identification, as specified in the User-Agent policy.

1. Get your credentials

The Wikimedia REST API does not require API keys or tokens. Instead, you should set a unique User-Agent or Api-User-Agent header in your requests to identify your application. There is no dashboard or specific process to obtain credentials.

2. Add them to .dlt/secrets.toml

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[sources.wikimedia_metrics_source]
user_agent = "your_user_agent_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 Wikimedia 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 wikimedia_metrics_pipeline.py

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

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Pipeline wikimedia_metrics_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset wikimedia_metrics_data
The duckdb destination used duckdb:/wikimedia_metrics.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 wikimedia_metrics_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 search_page and page_by_title from the Wikimedia 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 wikimedia_metrics_source(user_agent=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://en.wikipedia.org/api/rest_v1/",
            "auth": {
                "type": "none",
                "N/A (User-Agent is a header, not a token key)": user_agent,
            },
        },
        "resources": [
            {"name": "search_page", "endpoint": {"path": "search/page", "data_selector": "pages"}},
            {"name": "page_by_title", "endpoint": {"path": "page/{title}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wikimedia_metrics_pipeline",
        destination="duckdb",
        dataset_name="wikimedia_metrics_data",
    )
    load_info = pipeline.run(wikimedia_metrics_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("wikimedia_metrics_pipeline").dataset()
sessions_df = data.search_page.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM wikimedia_metrics_data.search_page LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("wikimedia_metrics_pipeline").dataset()
data.search_page.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 Wikimedia 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

Rate Limits

The Wikimedia REST API has rate limits to ensure fair usage and stability. While specific numerical limits are not explicitly detailed in the provided excerpts, the documentation mentions a 'Rate limits' policy. Exceeding these limits may result in temporary blocking or error responses.

User-Agent Policy

All requests are asked to set a unique User-Agent or Api-User-Agent header. Failure to comply with this policy may lead to requests being blocked or denied, as it is used for identification and adherence to usage policies.

Status Codes and Error Messages

The API provides various status codes and error messages to indicate issues with requests. Developers should consult the 'Status codes and error messages' section of the API documentation for detailed explanations and troubleshooting steps for specific error responses.

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