PyDigger Python API Docs | dltHub

PyDigger is a site/project for collecting and exposing metadata about Python packages and repositories (unearthing stuff in Python). The REST API base URL is https://www.diggernaut.com/api and Token-based authorization via an 'Authorization: Token ' HTTP 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 PyDigger data in under 10 minutes.

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What data can I load from PyDigger?

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

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How do I authenticate with the PyDigger API?

Include an HTTP header Authorization: Token <your_token>. Example: Authorization: Token 9944b09199c62bcf9418ad846dd0e4bbdfc6ee4b

1. Get your credentials

1. For self-hosted/dev use (PyDigger project): create a GitHub personal access token and place it into the project's dev.yml as the github-token (per repository README).
2. For Diggernaut API: sign up for a Diggernaut account (paid subscription required for some endpoints) and generate or obtain an API token in your user account/dashboard to use in the Authorization header. (Diggernaut docs indicate use of "Authorization: Token ").

2. Add them to .dlt/secrets.toml

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[sources.pydigger_keywords_source]
token = "your_api_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 PyDigger 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 pydigger_keywords_pipeline.py

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

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Pipeline pydigger_keywords_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset pydigger_keywords_data
The duckdb destination used duckdb:/pydigger_keywords.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 pydigger_keywords_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 digger_config and digger_start from the PyDigger 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 pydigger_keywords_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://www.diggernaut.com/api",
            "auth": {
                "type": "api_key",
                "token": token,
            },
        },
        "resources": [
            {"name": "digger_config", "endpoint": {"path": "diggers/:id/config", "data_selector": "config"}},
            {"name": "digger_start", "endpoint": {"path": "diggers/:id/start", "data_selector": "Status"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="pydigger_keywords_pipeline",
        destination="duckdb",
        dataset_name="pydigger_keywords_data",
    )
    load_info = pipeline.run(pydigger_keywords_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("pydigger_keywords_pipeline").dataset()
sessions_df = data.digger_config.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM pydigger_keywords_data.digger_config LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("pydigger_keywords_pipeline").dataset()
data.digger_config.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 PyDigger 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

Authorization failures

If you receive 401/403 responses, ensure the Authorization header is set exactly as: Authorization: Token . Verify the token is active and has the required access (some digger endpoints require paid subscription).

Rate limits and access levels

Some endpoints are restricted by account/subscription. The Diggernaut docs indicate "Access Level: User with paid subscription" for operations such as starting a digger. If the API returns errors about limits or access, verify your plan and contact Diggernaut support.

Response shape and selectors

Example responses in docs show JSON objects (e.g. {"config": "---\ndo:..."} and {"Status": "Digger has been scheduled for start"}). Expect object responses rather than top-level arrays; use the exact key names (e.g. "config", "Status") as data selectors in dlt pipeline definitions.

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