MediaWiki Python API Docs | dltHub

MediaWiki REST API is a RESTful interface that provides access to MediaWiki content and metadata (pages, revisions, files, search/autocomplete, etc.) in JSON or HTML formats. The REST API base URL is https://{site}/w/rest.php/v1 and supports OAuth (Bearer) and cookie-based auth; many read endpoints require no auth for public projects..

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 MediaWiki data in under 10 minutes.

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What data can I load from MediaWiki?

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

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How do I authenticate with the MediaWiki API?

Write and user-scoped endpoints use OAuth 2.0 / OAuth extension (Bearer token in Authorization header) or cookie-based authentication with CSRF tokens. For OAuth use Authorization: Bearer . Cookie-based requests must include a CSRF token obtained from the Action API when required.

1. Get your credentials

1. Register an application or obtain OAuth credentials per your MediaWiki/Wikimedia deployment (see Extension:OAuth or Wikimedia API portal). 2) Perform OAuth authorization flow (client credentials or other supported flow) to obtain an access token. 3) Use the token in the Authorization header: Authorization: Bearer . For cookie-based auth: log in with Action API, then request a CSRF token via Action API Token endpoint and include token in REST request body as 'token'.

2. Add them to .dlt/secrets.toml

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[sources.mediawiki_rest_api_source]
bearer_token = "your_oauth_bearer_token_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 MediaWiki 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 mediawiki_rest_api_pipeline.py

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

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Pipeline mediawiki_rest_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mediawiki_rest_api_data
The duckdb destination used duckdb:/mediawiki_rest_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 mediawiki_rest_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 page_html and search/title from the MediaWiki 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 mediawiki_rest_api_source(bearer_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{site}/w/rest.php/v1",
            "auth": {
                "type": "bearer",
                "token": bearer_token,
            },
        },
        "resources": [
            {"name": "page_html", "endpoint": {"path": "page/{title}/html", "data_selector": "html"}},
            {"name": "autocomplete", "endpoint": {"path": "search/title", "data_selector": "pages"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mediawiki_rest_api_pipeline",
        destination="duckdb",
        dataset_name="mediawiki_rest_api_data",
    )
    load_info = pipeline.run(mediawiki_rest_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("mediawiki_rest_api_pipeline").dataset()
sessions_df = data.page_html.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mediawiki_rest_api_data.page_html LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mediawiki_rest_api_pipeline").dataset()
data.page_html.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 MediaWiki 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

• Symptom: 401 Unauthorized or 400 Missing token. Cause: missing or invalid Authorization Bearer token for OAuth-protected endpoints or missing CSRF token when using cookie auth. Fix: add Authorization: Bearer for OAuth; for cookie auth obtain CSRF token from Action API and include as 'token' in request body.

Edit conflicts and 409 responses

• When updating pages, the API requires the latest revision id; if latest.id doesn't match the current revision, the API may return 409 (edit conflict). Fix: fetch latest page source to obtain latest.id, then retry update with correct latest.id.

Unsupported Content-Type and 415

• Ensure requests sending JSON include Content-Type: application/json. For page updates include source and required fields.

Pagination and large result handling

• Many endpoints return arrays inside wrapper objects (e.g., pages, files). For large results endpoints use query parameters like limit (1100) where provided; follow API responses and the schema; some endpoints may return continuation or truncated results and a 500 for too-large requests (e.g., page with >100 media files returns 500).

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